Bridder

bridder

My Thoughts to Protect Privacy A mask designed to deny AI-based facial recognition from all angles. I have always been a pretty private person, but lately, the weight of it feels as unbearable as ever. I just can’t seem to get out with a feeling of people watching me every time. It is no paranoia but just the plain reality of living in a place where face recognition has become an easy technological tool. And when you turn around, the shiny lenses of cameras pop up everywhere-from street corners and shop windows to public events. It is as if my face has become a commodity something that is catalogued and analyzed. I remember when I first heard about AI facial recognition systems. It was a news update, on a cold autumn evening. I was holding hot tea, covered by a blanket, scanning my phone as it scrolled its screen. The anchor spoke confidently about such systems wherein one could identify a person in seconds, revealing his personal history and preferences without ever knowing him or her. Shivers run through my spine. Over time, it had nothing to do with cameras; it’s who I am, my preferences, and -sold to the highest bidder. Already Available on Amazon Yelo Pomelo Anti Surveillance Neck Gaiter, Anti Facial Recognition Mask and Headband, Face Protest facial recognition tech by wearing this anti-surveillance neck gaiter. The more you know, the more you feel that you are one rat in an experiment into which you certainly have yet to sign up. Every day, you hear stories of people whose lives have been picked apart by algorithms because they happened to pass in front of surveillance cameras. I could envision my face torn into pieces. All the wrinkles and blemishes would be magnified, and reduced to mere data points. It was like living in some science fiction dystopia where the government and corporations pulled all the strings. Everything I said would not matter. Somehow it pierced my life: the thought of being checked on all the time. I stopped walking around places, and wouldn’t even go to the park with “intelligent” cameras installed; instead, only shop from small stores where no security footage was even installed. It was draining. I would consider changing my appearance completely by getting a new hair color and wearing different hats and sunglasses just so I’d feel like I ain't being exposed so much. Deep down, however, I realized that wasn't just a Band-Aid on a much bigger issue. Something appeared on the internet one afternoon: a mask designed to evade facial recognition. I read through all the details about it greedily: special confusing materials for algorithms, interesting patterns distorting features, and even changing colors with light. This is something straight out of a future film, and my heart skips a beat. Perhaps this was my chance to get my private life back. I told them I wanted one. And when it came, I ripped open the package and looked at it with eagerness and skepticism. It didn’t resemble anything I’d ever worn before, this psychotropic swirling look and shape. I put it on and looked into the mirror. It felt marvelous wearing something that was a symbol of resistance against constant surveillance. Finally a semblance of confidence after months, and this mask of theirs that’s now my defense against a world turned mad on breaking into one’s life. The next time I dared to go out, I wore the mask with pride. My head held high, I went to step out into the streets, feeling to myself that I was at least free to do as I pleased. Even so, passing by familiar buildings did not still the nagging doubt in the back of my head. What if this attracted more onlookers? Perhaps it would have made people start wondering about me. It was a time of empowerment and fear reminding me I was fighting a battle uphill against something invisible. I knew that something in the air around me had shifted because people looked at me with that unmistakable gaze; some stared while others whispered behind each other’s ears. There was no feeling of being safe anymore but exposed in a whole new way, for the mask became a beacon and a declaration of wariness of the world around me, and that alone seemed to be drawing attention. I realized at that moment that it wasn’t about staying secret, but rather about being seen. Not to attention to myself but just to walk around the world and not automatically be placed under the spotlight for others’ exhibitions. And I knew that although the mask protected me from recognition by AI, it could not save society from judgment and shame of my uniqueness. So, I tried to balance my two states of being. Less and less was I wearing the mask. In its place, a simple scarf or oversized sunglasses became my new accessory. My scope narrowed down to small things that helped keep my privacy: change messaging apps to encrypted services, block location-sharing features on your devices, and tell your community to ask for stronger privacy laws. Each movement, in its way, was a token of regained agency, but each battle seemed impossible to win. One night, over a cup of coffee in a café, I heard a group of friends grappling with the same fears as me. They talked about not feeling right with surveillance on them and began gathering stories of moments when they felt viewed in locations where they probably shouldn’t be viewed. A relief-it wasn't just me. Ideas on how to deal and we spoke about the potential of obliterating technology rather than our rights. I realized through that conversation perhaps that I was suffering in a state of isolation, while a movement was growing of people who shared similar concerns. Maybe the future wasn’t as dark as I thought. The more people began talking about the need for private lives, the more this would breed an awareness of the need for solutions, perhaps like the anti-facial recognition mask, and then the change. I still fear to open my private life to the public, and my mask continues to remain within that journey. It is a reminder of where the battlefield lies-between liberties and technological surveillance. I cannot possibly destroy it from within, but I can fight for my right to exist without people watching me all the time. In such a digital world, it feels like such a heavy struggle to fight for privacies, but every little one counts. So, for now, I will continue to find more ways to protect my identity as I form bonds with people who share my concerns, hoping together we can make privacy not just a choice but something that is owed. My Thoughts to Protect Privacy A mask designed to deny AI-based facial recognition from all angles. I have always been a pretty private person, but lately, the weight of it feels as unbearable as ever. I just can’t seem to get out with a feeling of people watching me every time. It is no paranoia but just the plain reality of living in a place where face recognition has become an easy technological tool. And when you turn around, the shiny lenses of cameras pop up everywhere-from street corners and shop windows to public events. It is as if my face has become a commodity something that is catalogued and analyzed. I remember when I first heard about AI facial recognition systems. It was a news update, on a cold autumn evening. I was holding hot tea, covered by a blanket, scanning my phone as it scrolled its screen. The anchor spoke confidently about such systems wherein one could identify a person in seconds, revealing his personal history and preferences without ever knowing him or her. Shivers run through my spine. Over time, it had nothing to do with cameras; it’s who I am, my preferences, and -sold to the highest bidder. Already Available on Amazon Yelo Pomelo Anti Surveillance Neck Gaiter, Anti Facial Recognition Mask and Headband, Face Protest facial recognition tech by wearing this anti-surveillance neck gaiter. Yelo Pomelo Anti Surveillance Neck Gaiter, Anti Facial Recognition Mask and Headband, Face Protest facial recognition tech by wearing this anti-surveillance neck gaiter. The more you know, the more you feel that you are one rat in an experiment into which you certainly have yet to sign up. Every day, you hear stories of people whose lives have been picked apart by algorithms because they happened to pass in front of surveillance cameras. I could envision my face torn into pieces. All the wrinkles and blemishes would be magnified, and reduced to mere data points. It was like living in some science fiction dystopia where the government and corporations pulled all the strings. Everything I said would not matter. Somehow it pierced my life: the thought of being checked on all the time. I stopped walking around places, and wouldn’t even go to the park with “intelligent” cameras installed; instead, only shop from small stores where no security footage was even installed. It was draining. I would consider changing my appearance completely by getting a new hair color and wearing different hats and sunglasses just so I’d feel like I ain't being exposed so much. Deep down, however, I realized that wasn't just a Band-Aid on a much bigger issue. Something appeared on the internet one afternoon: a mask designed to evade facial recognition. I read through all the details about it greedily: special confusing materials for algorithms, interesting patterns distorting features, and even changing colors with light. This is something straight out of a future film, and my heart skips a beat. Perhaps this was my chance to get my private life back. I told them I wanted one. And when it came, I ripped open the package and looked at it with eagerness and skepticism. It didn’t resemble anything I’d ever worn before, this psychotropic swirling look and shape. I put it on and looked into the mirror. It felt marvelous wearing something that was a symbol of resistance against constant surveillance. Finally a semblance of confidence after months, and this mask of theirs that’s now my defense against a world turned mad on breaking into one’s life. The next time I dared to go out, I wore the mask with pride. My head held high, I went to step out into the streets, feeling to myself that I was at least free to do as I pleased. Even so, passing by familiar buildings did not still the nagging doubt in the back of my head. What if this attracted more onlookers? Perhaps it would have made people start wondering about me. It was a time of empowerment and fear reminding me I was fighting a battle uphill against something invisible. I knew that something in the air around me had shifted because people looked at me with that unmistakable gaze; some stared while others whispered behind each other’s ears. There was no feeling of being safe anymore but exposed in a whole new way, for the mask became a beacon and a declaration of wariness of the world around me, and that alone seemed to be drawing attention. I realized at that moment that it wasn’t about staying secret, but rather about being seen. Not to attention to myself but just to walk around the world and not automatically be placed under the spotlight for others’ exhibitions. And I knew that although the mask protected me from recognition by AI, it could not save society from judgment and shame of my uniqueness. So, I tried to balance my two states of being. Less and less was I wearing the mask. In its place, a simple scarf or oversized sunglasses became my new accessory. My scope narrowed down to small things that helped keep my privacy: change messaging apps to encrypted services, block location-sharing features on your devices, and tell your community to ask for stronger privacy laws. Each movement, in its way, was a token of regained agency, but each battle seemed impossible to win. One night, over a cup of coffee in a café, I heard a group of friends grappling with the same fears as me. They talked about not feeling right with surveillance on them and began gathering stories of moments when they felt viewed in locations where they probably shouldn’t be viewed. A relief-it wasn't just me. Ideas on how to deal and we spoke about the potential of obliterating technology rather than our rights. I realized through that conversation perhaps that I was suffering in a state of isolation, while a movement was growing of people who shared similar concerns. Maybe the future wasn’t as dark as I thought. The more people began talking about the need for private lives, the more this would breed an awareness of the need for solutions, perhaps like the anti-facial recognition mask, and then the change. I still fear to open my private life to the public, and my mask continues to remain within that journey. It is a reminder of where the battlefield lies-between liberties and technological surveillance. I cannot possibly destroy it from within, but I can fight for my right to exist without people watching me all the time. In such a digital world, it feels like such a heavy struggle to fight for privacies, but every little one counts. So, for now, I will continue to find more ways to protect my identity as I form bonds with people who share my concerns, hoping together we can make privacy not just a choice but something that is owed.

An Anti-Facial Recognition Mask: Fighting for Privacy

According to political reporter Jake Evans if you’re an Australian adult on Facebook, your public photos, posts, and other data are being scraped to train their AI models. That’s the admission from Facebook, as revealed in a recent inquiry. Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Unlike the EU, which gets the luxury of an opt-out option, Australians are left in the lurch. Facebook hasn’t been required to provide such an option under local privacy laws, and when asked if one might be introduced, representatives remained noncommittal. Meta’s global privacy director, Melinda Claybaugh, faced questions during the inquiry about whether Facebook is harvesting the data of every Australian to enhance its generative AI tools. Initially, Claybaugh denied this, but the situation quickly unraveled. Labor senator Tony Sheldon probed whether Meta had been using Australian posts dating back to 2007 for its AI products. Claybaugh replied with a firm “we have not done that.” But Greens senator David Shoebridge was ready to challenge that assertion. Shoebridge didn’t hold back: "The truth of the matter is that unless you have consciously set those posts to private since 2007, Meta has just decided that you will scrape all of the photos and all of the texts from every public post on Instagram or Facebook since 2007, unless there was a conscious decision to set them on private. That’s the reality, isn’t it?” Claybaugh’s response? “Correct.” Claybaugh also noted that while accounts of those under 18 are not scraped, public photos of children on accounts like Senator Sheldon’s would indeed be. The Facebook representative couldn't clarify whether data from users who were underage when they created their accounts but are now adults had been scraped. In Europe, Meta has been upfront about using user data to train its AI products, with an opt-out option provided due to stringent privacy laws. According to Claybaugh, this is because European privacy laws are still being interpreted in the context of AI training. "In Europe there is an ongoing legal question around what is the interpretation of existing privacy law with respect to AI training," Claybaugh says. "We have paused launching our AI products in Europe while there is a lack of certainty. So you are correct that we are offering an opt-out to users in Europe. "“I will say that the ongoing conversation in Europe is the direct result of the existing regulatory landscape." The development comes on the heels of the federal government’s pledge to ban social media for children, citing the platforms’ potential harms. Senator Shoebridge told the ABC that if the government is serious about addressing online harms, overhauling privacy laws should be a top priority. “There’s a reason that people’s privacy is protected in Europe and not in Australia,” Shoebridge says. “It’s because European lawmakers made tough privacy laws. Meta made it clear today that if Australia had these same laws, Australians’ data would also have been protected.” “The government’s failure to act on privacy means companies like Meta are continuing to monetize and exploit pictures and videos of children on Facebook.” He added, Facebook's so-called data scraping and its so-called benefits Let’s get real about Facebook's so-called data scraping and its so-called benefits. You’ve got this colossal social media behemoth, scraping every public photo and post of Australians with a voracious appetite. They claim it’s all about training their AI models—whatever that means. But despite their relentless data harvesting, the platform can’t seem to stop fake profiles and scams from running rampant. Every day, countless fake profiles are reported, but guess what? The algorithms that are supposed to protect us? They’re about as effective as a screen door on a submarine. These fraudulent accounts, fueled by stolen images, slip through the cracks, operating like they're invisible. Even after being reported, they continue to thrive. So, what exactly is this data being used for if it can’t even help keep scammers at bay? It's become glaringly obvious that Facebook’s grand data scheme isn’t about user safety—it’s about profit. The platform scoops up your private data and photos, monetizes it to make billions, and you? You get nothing in return. Nada. Not even a thank you. So, let’s be honest here. Why should we trust a platform that exploits our personal information for its own gain while failing miserably at addressing real issues like online fraud? If you’re still posting personal data on Facebook, it’s time to think twice. Your private data deserves better protection than what this multi-billion-dollar platform offers. Keep your photos and info away from Facebook, because clearly, they’re not using it to make the platform any safer for you. What data scraping really is. Alright, let’s cut through the jargon and get down to what data scraping really is. Imagine Facebook has this gigantic vacuum cleaner, and it’s set to suck up every bit of data it can find—from your public photos to your posts and everything in between. That’s data scraping in a nutshell. When we talk about data scraping, we’re talking about the process where a company like Facebook uses automated tools to collect massive amounts of data from its users. They don’t ask for your permission (however you do need to read the fine print); they just scoop up everything that’s publicly available. It’s like having a nosy neighbor who’s not only peeking through your windows but also collecting every scrap of information they can about you. The goal? To use this data for a variety of purposes—often to train their AI models, target ads, or even build detailed user profiles that can be sold to the highest bidder. But here’s the deal, while Facebook’s vacuum cleaner is busy hoovering up your information, it doesn’t seem too concerned about using it to tackle real problems like fake profiles or scams. The government is expected to announce long-awaited reforms to the Privacy Act soon, following a 2020 review that deemed the current laws outdated. Attorney-General Mark Dreyfus had previously indicated that the new legislation would be announced in August. According to political reporter Jake Evans if you’re an Australian adult on Facebook, your public photos, posts, and other data are being scraped to train their AI models. That’s the admission from Facebook, as revealed in a recent inquiry. Jake Evans Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: This story represents the views of the author of the story. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Unlike the EU, which gets the luxury of an opt-out option, Australians are left in the lurch. Facebook hasn’t been required to provide such an option under local privacy laws, and when asked if one might be introduced, representatives remained noncommittal. Meta’s global privacy director, Melinda Claybaugh, faced questions during the inquiry about whether Facebook is harvesting the data of every Australian to enhance its generative AI tools . Initially, Claybaugh denied this, but the situation quickly unraveled. generative AI tools Labor senator Tony Sheldon probed whether Meta had been using Australian posts dating back to 2007 for its AI products. Claybaugh replied with a firm “we have not done that.” But Greens senator David Shoebridge was ready to challenge that assertion. Shoebridge didn’t hold back: "The truth of the matter is that unless you have consciously set those posts to private since 2007, Meta has just decided that you will scrape all of the photos and all of the texts from every public post on Instagram or Facebook since 2007, unless there was a conscious decision to set them on private. That’s the reality, isn’t it?” Claybaugh’s response? “Correct.” Claybaugh’s response? “Correct.” Claybaugh’s response? “Correct.” Claybaugh also noted that while accounts of those under 18 are not scraped, public photos of children on accounts like Senator Sheldon’s would indeed be. The Facebook representative couldn't clarify whether data from users who were underage when they created their accounts but are now adults had been scraped. adults In Europe, Meta has been upfront about using user data to train its AI products, with an opt-out option provided due to stringent privacy laws. According to Claybaugh, this is because European privacy laws are still being interpreted in the context of AI training. "In Europe there is an ongoing legal question around what is the interpretation of existing privacy law with respect to AI training," Claybaugh says. "We have paused launching our AI products in Europe while there is a lack of certainty. So you are correct that we are offering an opt-out to users in Europe. "“I will say that the ongoing conversation in Europe is the direct result of the existing regulatory landscape." The development comes on the heels of the federal government’s pledge to ban social media for children, citing the platforms’ potential harms. Senator Shoebridge told the ABC that if the government is serious about addressing online harms, overhauling privacy laws should be a top priority. “There’s a reason that people’s privacy is protected in Europe and not in Australia,” Shoebridge says. “It’s because European lawmakers made tough privacy laws. Meta made it clear today that if Australia had these same laws, Australians’ data would also have been protected.” “The government’s failure to act on privacy means companies like Meta are continuing to monetize and exploit pictures and videos of children on Facebook.” He added, Facebook's so-called data scraping and its so-called benefits Let’s get real about Facebook's so-called data scraping and its so-called benefits. You’ve got this colossal social media behemoth, scraping every public photo and post of Australians with a voracious appetite. They claim it’s all about training their AI models—whatever that means. But despite their relentless data harvesting, the platform can’t seem to stop fake profiles and scams from running rampant. scams Every day, countless fake profiles are reported, but guess what? The algorithms that are supposed to protect us? They’re about as effective as a screen door on a submarine. These fraudulent accounts, fueled by stolen images, slip through the cracks, operating like they're invisible. fake profiles Even after being reported, they continue to thrive. So, what exactly is this data being used for if it can’t even help keep scammers at bay? keep scammers It's become glaringly obvious that Facebook’s grand data scheme isn’t about user safety—it’s about profit. The platform scoops up your private data and photos, monetizes it to make billions, and you? You get nothing in return. Nada. Not even a thank you. So, let’s be honest here. Why should we trust a platform that exploits our personal information for its own gain while failing miserably at addressing real issues like online fraud? If you’re still posting personal data on Facebook, it’s time to think twice. Your private data deserves better protection than what this multi-billion-dollar platform offers. Keep your photos and info away from Facebook, because clearly, they’re not using it to make the platform any safer for you. What data scraping really is. Alright, let’s cut through the jargon and get down to what data scraping really is. Imagine Facebook has this gigantic vacuum cleaner, and it’s set to suck up every bit of data it can find—from your public photos to your posts and everything in between. That’s data scraping in a nutshell. When we talk about data scraping, we’re talking about the process where a company like Facebook uses automated tools to collect massive amounts of data from its users. They don’t ask for your permission (however you do need to read the fine print); they just scoop up everything that’s publicly available. It’s like having a nosy neighbor who’s not only peeking through your windows but also collecting every scrap of information they can about you. The goal? To use this data for a variety of purposes—often to train their AI models, target ads, or even build detailed user profiles that can be sold to the highest bidder. But here’s the deal, while Facebook’s vacuum cleaner is busy hoovering up your information, it doesn’t seem too concerned about using it to tackle real problems like fake profiles or scams. The government is expected to announce long-awaited reforms to the Privacy Act soon, following a 2020 review that deemed the current laws outdated. Attorney-General Mark Dreyfus had previously indicated that the new legislation would be announced in August.

Facebook Confirms It Scrapes Every Australian Adult's Public Photos and Posts to Train AI

Author:
(1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Table of Links Abstract and 1. A Hymn 2. Asymmetry 3. Bubbles, Guinea Pigs 4. Interface and Data Biopolitics 5. Conclusions: Implications for Design and References Abstract This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means. A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence. Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights. A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways. Keywords: Hyperconnectivity, Algorithms, Biopolitics, Ethics, Data 1. A Hymn In her “Hymn of Acxiom” folk singer Vienna Teng (2013) starts off with lyrics “Somebody hears you, you know that…”, in what seems to be a church choir. After listening for a bit, the real topic the artist is discussing about becomes clear: Acxiom is not a benevolent divinity somewhere in the cosmosphere caringly waiting to hear the troubles of his beloved human beings, but, rather, a highpowered data broker which has been described as “the Private NSA” (Tom’s Guide, 2013), as the silent, largest consumer data processor in the world (Fortune Magazine, 2004) and as “Big Brother in Arkansas” (NY Times, 2012). The topic of the song is data-surveillance. The idea for the song came while the author was pursuing an MBA at the University of Michigan: a colleague working with Acxiom data was shocked about the amount of information the company had available about herself and her husband. An interesting thing about the song is that the creepy, Orwellian, lyrics also empathize with databases as well as excoriating them. This is, in fact, an interesting point of view. As, on the one hand, we – directly and indirectly – consent tour data to be collected through our behaviors and basically accepting our lifestyle, on the other hand we feel deeply uneasy about that and from its implications. As we benefit from enterprises being able to provide us with products and services which are “more relevant” for us (more on this later in the article), we are simultaneously wary of the fact that a single subject knows so much about us and uses it to “sell us” to the highest bidder, and what this possibility implies for our liberties, freedoms and rights. Even more, it is progressively harder, if not impossible, for us, to know and understand what parts of our online and offline environment are determined from these data collection processes, or about which subjects have this data about us available, or how they are using it and for what purposes (Lafrance, 2014). In fact, the entries we see when we browse search engines, social media websites, and other online services are completely determined by these processes: the operators of these services feed the data they acquired about us to their classification algorithms which, in return, use it to determine what we may be more and less interested about, or where their optimal business opportunities lay in relation to our profile. This is not only true for our online lives: the offline world is quickly catching up. Physical mailings; fidelity cards; algorithmic research and control applied to stores, services and spaces of the city; imaging through security cameras; object and facial recognition on devices and architectures; the Internet of Things (IoT) and its sensors, possibilities for user identification and biometrics. These all combine with a plethora of other options which are turning us into data subjects, which can be recognized and tracked on databases as in the physical world. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Author: Author: (1) Salvatore Iaconesi, ISIA Design Florence and *Corresponding author (salvatore.iaconesi@artisopensource.net). Table of Links Abstract and 1. A Hymn Abstract and 1. A Hymn 2. Asymmetry 2. Asymmetry 3. Bubbles, Guinea Pigs 3. Bubbles, Guinea Pigs 4. Interface and Data Biopolitics 4. Interface and Data Biopolitics 5. Conclusions: Implications for Design and References 5. Conclusions: Implications for Design and References Abstract This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means. A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence. Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights. A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways. Keywords : Hyperconnectivity, Algorithms, Biopolitics, Ethics, Data Keywords 1. A Hymn In her “Hymn of Acxiom” folk singer Vienna Teng (2013) starts off with lyrics “Somebody hears you, you know that…”, in what seems to be a church choir. After listening for a bit, the real topic the artist is discussing about becomes clear: Acxiom is not a benevolent divinity somewhere in the cosmosphere caringly waiting to hear the troubles of his beloved human beings, but, rather, a highpowered data broker which has been described as “the Private NSA” (Tom’s Guide, 2013), as the silent, largest consumer data processor in the world (Fortune Magazine, 2004) and as “Big Brother in Arkansas” (NY Times, 2012). The topic of the song is data-surveillance. The idea for the song came while the author was pursuing an MBA at the University of Michigan: a colleague working with Acxiom data was shocked about the amount of information the company had available about herself and her husband. An interesting thing about the song is that the creepy, Orwellian, lyrics also empathize with databases as well as excoriating them. This is, in fact, an interesting point of view. As, on the one hand, we – directly and indirectly – consent tour data to be collected through our behaviors and basically accepting our lifestyle, on the other hand we feel deeply uneasy about that and from its implications. As we benefit from enterprises being able to provide us with products and services which are “more relevant” for us (more on this later in the article), we are simultaneously wary of the fact that a single subject knows so much about us and uses it to “sell us” to the highest bidder, and what this possibility implies for our liberties, freedoms and rights. Even more, it is progressively harder, if not impossible, for us, to know and understand what parts of our online and offline environment are determined from these data collection processes, or about which subjects have this data about us available, or how they are using it and for what purposes (Lafrance, 2014). In fact, the entries we see when we browse search engines, social media websites, and other online services are completely determined by these processes: the operators of these services feed the data they acquired about us to their classification algorithms which, in return, use it to determine what we may be more and less interested about, or where their optimal business opportunities lay in relation to our profile. This is not only true for our online lives: the offline world is quickly catching up. Physical mailings; fidelity cards; algorithmic research and control applied to stores, services and spaces of the city; imaging through security cameras; object and facial recognition on devices and architectures; the Internet of Things (IoT) and its sensors, possibilities for user identification and biometrics. These all combine with a plethora of other options which are turning us into data subjects, which can be recognized and tracked on databases as in the physical world. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Interface and Data Biopolitics in the Age of Hyperconnectivity: Abstract and A Hymn

Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district. A supply chain attack is one method that has been gaining a lot of traction recently, whereby an attack on an outside provider can lead to the attacker being able to infiltrate the school’s digital infrastructure. If the outside provider has been granted access rights to use the network or other applications, then the attacker would only need to get through the third party’s defenses to infiltrate the school’s system. This type of attack is particularly interesting to criminals—and devastating to victims—because they can gain access to multiple districts and mountains of data through one vendor. Types of supply chain attacks include stealing security certificates, compromising software development tools, preinstalling malware on devices, and embedding malicious code in firmware. For example, if a hacker steals a certificate used to authenticate a software update, they can use it to distribute malware disguised as a legitimate update. In schools, where IT resources may already be stretched thin, detecting these kinds of sophisticated attacks is very difficult. Supply chain attacks are very broad and can impact organizations of all types and sizes. Let’s examine where they tend to come from and their impact before discussing how schools can better defend themselves. The Source of Supply Chain Attacks Supply chain attacks can come from a variety of places and, depending on the source, can bring their own set of risks. Commercial software is something that every school will have across its network, and hackers can insert malicious code into a common software application that would then gain access to any school using that particular software. The problem is that schools often depend on software updates to patch vulnerabilities, but the updates themselves are actually one of the most common areas to exploit. One of the most famous examples came a few years back in 2020, when the major software company, __SolarWinds, was hacked with a backdoor __inserted into one of their IT management tools, and thousands of government organizations and schools around the world were affected.Another common source comes from open-source software where schools use these solutions as they’re generally more scalable and cost-effective, but this can also leave them more vulnerable. During the development process, hackers can introduce vulnerabilities, and then they wreak havoc when the software is finally implemented. There was a clear incident of this earlier this year when a software engineer found a backdoor that had gone unnoticed after two years of a group calledJia Tan implementing versions of XZ Utils, providing hours of largely benign volunteer coding into the open-source software, Linux. Lastly, we need to touch on foreign-sourced threats, which can be dangerous when schools are using systems from outside the country in their networks. Some countries have varying controls on tech industries, so schools need to be careful. What is the Impact of Supply Chain Attacks on Schools? There can be a huge impact on schools that are victims of supply chain attacks, including the disruption of administrative functions and the educational process. One of the biggest risks obviously comes from data breaches, where student records, financial details, and even potentially health data can be leaked. This could easily lead to privacy violations and identity theft.Schools also depend on software for scheduling, grading, and virtual classrooms, and if that is compromised, it can cause huge interruptions, with students and teachers unable to access vital resources. Lastly, financial costs, although seemingly not as prevalent in a supply chain attack, can still be a consequence. Ransomware can lock critical systems through compromised software. Even if the ransom is not paid, restoring systems and implementing stronger security measures is expensive. What Are The Mitigation Strategies? There are various ways that schools can go about mitigating supply chain attacks, primarily schools must have cybersecurity measures in place to protect their systems. They are outlined by the Department for Education, and involve a range of steps, from simple steps such as adopting multi-factor authentication to more complex data protection impact assessments. Regular software updates are important, but they are far from being fool-proof, schools need to be informed about any potential vulnerabilities that could arise in the software that they use. This can be done by monitoring cyber news and having open lines of communication with their vendors to stay on top of emerging threats. Access control is another element that can’t go unnoticed. Schools need to always review who has access to their systems and ensure that permissions are limited to the necessary people. The final mitigation strategy to mention is testing out incident response plans. This should involve explaining what steps would need to be taken in the event of a supply chain attack and how to isolate compromised systems, restore data from backups, and communicate with stakeholders. These kinds of drills mean that staff are ready to act quickly and effectively when an attack occurs. Supply chain attacks are undoubtedly brutal, and hackers can exploit weaknesses in schools, and their impact can be devastating. However, following some of the steps outlined above can at least mitigate the potential impact if staff are dedicated to prioritizing cybersecurity and good hygiene. Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district. Schools have to play a crucial role in safeguarding children from all kinds of threats, and one that often flies under the radar is cyber attacks. Unfortunately, schools are a gold mine for criminals with invaluable data related to personal information and financial details which can be sold to the highest bidder on the black market. This was the case towards the end of last year, as thousands of students’ data was leaked in the Louisiana school district . leaked in the Louisiana school district leaked in the Louisiana school district A supply chain attack is one method that has been gaining a lot of traction recently, whereby an attack on an outside provider can lead to the attacker being able to infiltrate the school’s digital infrastructure. If the outside provider has been granted access rights to use the network or other applications, then the attacker would only need to get through the third party’s defenses to infiltrate the school’s system. This type of attack is particularly interesting to criminals—and devastating to victims—because they can gain access to multiple districts and mountains of data through one vendor. Types of supply chain attacks include stealing security certificates, compromising software development tools, preinstalling malware on devices, and embedding malicious code in firmware. For example, if a hacker steals a certificate used to authenticate a software update, they can use it to distribute malware disguised as a legitimate update. In schools, where IT resources may already be stretched thin, detecting these kinds of sophisticated attacks is very difficult. Supply chain attacks are very broad and can impact organizations of all types and sizes. Let’s examine where they tend to come from and their impact before discussing how schools can better defend themselves. The Source of Supply Chain Attacks Supply chain attacks can come from a variety of places and, depending on the source, can bring their own set of risks. Commercial software is something that every school will have across its network, and hackers can insert malicious code into a common software application that would then gain access to any school using that particular software. The problem is that schools often depend on software updates to patch vulnerabilities, but the updates themselves are actually one of the most common areas to exploit. One of the most famous examples came a few years back in 2020, when the major software company, __ SolarWinds, was hacked with a backdoor __inserted into one of their IT management tools, and thousands of government organizations and schools around the world were affected. Another common source comes from open-source software where schools use these solutions as they’re generally more scalable and cost-effective, but this can also leave them more vulnerable. During the development process, hackers can introduce vulnerabilities, and then they wreak havoc when the software is finally implemented. There was a clear incident of this earlier this year when a software engineer found a backdoor that had gone unnoticed after two years of a group called Jia Tan implementing versions of XZ Utils , providing hours of largely benign volunteer coding into the open-source software, Linux. SolarWinds, was hacked with a backdoor Jia Tan implementing versions of XZ Utils Jia Tan implementing versions of XZ Utils Lastly, we need to touch on foreign-sourced threats, which can be dangerous when schools are using systems from outside the country in their networks. Some countries have varying controls on tech industries, so schools need to be careful. What is the Impact of Supply Chain Attacks on Schools? There can be a huge impact on schools that are victims of supply chain attacks, including the disruption of administrative functions and the educational process. One of the biggest risks obviously comes from data breaches, where student records, financial details, and even potentially health data can be leaked. This could easily lead to privacy violations and identity theft. Schools also depend on software for scheduling, grading, and virtual classrooms, and if that is compromised, it can cause huge interruptions, with students and teachers unable to access vital resources. Lastly, financial costs, although seemingly not as prevalent in a supply chain attack, can still be a consequence. Ransomware can lock critical systems through compromised software. Even if the ransom is not paid, restoring systems and implementing stronger security measures is expensive. What Are The Mitigation Strategies? There are various ways that schools can go about mitigating supply chain attacks, primarily schools must have cybersecurity measures in place to protect their systems. They are outlined by the Department for Education , and involve a range of steps, from simple steps such as adopting multi-factor authentication to more complex data protection impact assessments. outlined by the Department for Education outlined by the Department for Education Regular software updates are important, but they are far from being fool-proof, schools need to be informed about any potential vulnerabilities that could arise in the software that they use. This can be done by monitoring cyber news and having open lines of communication with their vendors to stay on top of emerging threats. Access control is another element that can’t go unnoticed. Schools need to always review who has access to their systems and ensure that permissions are limited to the necessary people. The final mitigation strategy to mention is testing out incident response plans. This should involve explaining what steps would need to be taken in the event of a supply chain attack and how to isolate compromised systems, restore data from backups, and communicate with stakeholders. These kinds of drills mean that staff are ready to act quickly and effectively when an attack occurs. Supply chain attacks are undoubtedly brutal, and hackers can exploit weaknesses in schools, and their impact can be devastating. However, following some of the steps outlined above can at least mitigate the potential impact if staff are dedicated to prioritizing cybersecurity and good hygiene.

How Can Schools Be Better Prepared for Supply Chain Attacks?

Google Failed To Show Effective “Competition For The Contract”

Google’s Contracts Reduce Investment And Innovation Among Market Participants

Cyber threats are just as constant as they are sophisticated, so safeguarding our online presence has never been more critical than ever before. But there’s decentralized AI that can tweak existing systems and represent a seismic shift in how we approach digital security. It’s here to make sure that our data stays ours and is not up for grabs by the highest bidder. Now, the question is, how does it make a difference and set a new standard for online safety? No More Single Points of Failure In traditional, centralized AI systems, all data processing, storage, and decision-making are concentrated within a central hub or server. While this centralization is efficient for managing resources and operations, it creates a significant vulnerability: if a cybercriminal successfully breaches this central hub, they gain access to the entire system. All of these can ultimately lead to catastrophic outcomes, such as widespread data breaches, unauthorized access to sensitive information, or even complete system shutdowns. Decentralized AI, however, distributes these functions across a network of independent nodes, each of which operates autonomously yet remains interconnected with the rest of the network. With this decentralized model, there is no single central hub that can be targeted and compromised. Instead, the system's intelligence and processing power are spread out. It makes sure that the failure or compromise of one node does not jeopardize the entire network. This distribution of resources means that even if a cyber attack successfully infiltrates one part of the network, the impact is contained and isolated. The compromised node can be quickly identified and neutralized, while the rest of the network continues to function securely and efficiently. Real-Time Threat Detection and Response Traditional AI systems also often face limitations in their ability to swiftly detect and counteract emerging threats. It can create bottlenecks, slowing down the process of identifying, analyzing, and responding to potential cyber-attacks. This delay, later, can give cybercriminals a critical window of opportunity to exploit vulnerabilities before defensive measures are implemented. Meanwhile, decentralized AI leverages its distributed network of nodes to enable faster and more efficient threat detection and response. Each node in a decentralized AI system operates independently, continuously monitoring its environment for suspicious activity or anomalies. When a potential threat is detected by one node, the information is instantly shared across the network, allowing other nodes to adjust their defenses in real-time. Also, decentralized AI's distributed architecture allows it to learn and adapt dynamically as new threats emerge. Each node contributes to a collective intelligence that grows stronger over time. For example, if a particular type of attack is detected in one part of the network, the knowledge and countermeasures developed in response to that attack are immediately shared with all other nodes. Resilience Against Distributed Denial of Service (DDoS) Attacks When a DDoS attack happens, cybercriminals flood a network, server, or website with an overwhelming amount of traffic, causing it to become slow, unresponsive, or entirely unavailable to legitimate users and negatively impacting core web vitals. These attacks exploit the centralized nature of traditional systems, where all traffic is directed to a single point. This makes it easier for attackers to overload the system and bring it down. Decentralized AI mitigates these risks by distributing data processing and network traffic across a wide array of nodes. Instead of converging on one central server, all traffic is dispersed throughout the network, making it much harder for an attacker to generate enough traffic to disrupt the system. This distributed architecture means that even if a DDoS attack manages to overwhelm a portion of the network, the impact is localized and contained– to prevent the entire system from going offline. The other nodes in the network continue to operate normally and make sure that legitimate users can still access the services and data they need. So, in short: a decentralized structure creates multiple pathways for traffic to flow, which helps maintain service availability even during an attack. Better Collaborative Cybersecurity Efforts Cybersecurity measures are often siloed within individual organizations, with limited sharing of threat intelligence and defense strategies. This lack of collaboration can leave gaps in defenses and make it easier for cybercriminals to exploit vulnerabilities that might go unnoticed by isolated entities. Decentralized AI changes this dynamic by creating a networked environment where nodes, which could represent different organizations or even different sectors, are connected and able to share information in real-time. For example, if one organization’s node detects a new type of malware or identifies a phishing attempt, this information can be immediately communicated across the network. Other nodes can then update their defenses to guard against the same threat, even before they encounter it themselves. An interconnectivity like this allows for a more collective and coordinated approach to cybersecurity, where the experiences and insights gained by one node can benefit the entire network. Adaptive and Self-Healing Capabilities The self-healing capability of decentralized AI refers to its ability to automatically repair and recover from disruptions or attacks. We know that in a decentralized system, if a node is compromised or experiences a malfunction, it does not bring down the entire network. Instead, the system can isolate the affected node, mitigate the damage, and allow the other nodes to continue functioning normally. This isolation and repair process is facilitated by the decentralized nature of the network, where each node operates independently but in coordination with others. Let’s say a node is under attack and becomes unresponsive, the network then reroutes traffic and resources to maintain service continuity, while the compromised node is repaired or replaced. Decentralized AI: A Robust Defense Against Cyber Threats Decentralized AI fosters a more dynamic and resilient cybersecurity environment by enabling networks to operate independently yet synergistically. This paradigm shift moves beyond merely fortifying defenses. It encourages a proactive and intelligent approach to security, where systems can autonomously detect, adapt, and recover from threats. Cyber threats are just as constant as they are sophisticated, so safeguarding our online presence has never been more critical than ever before. But there’s decentralized AI that can tweak existing systems and represent a seismic shift in how we approach digital security. It’s here to make sure that our data stays ours and is not up for grabs by the highest bidder. Now, the question is, how does it make a difference and set a new standard for online safety? No More Single Points of Failure No More Single Points of Failure In traditional, centralized AI systems, all data processing, storage, and decision-making are concentrated within a central hub or server. While this centralization is efficient for managing resources and operations, it creates a significant vulnerability: if a cybercriminal successfully breaches this central hub, they gain access to the entire system. cybercriminal All of these can ultimately lead to catastrophic outcomes, such as widespread data breaches, unauthorized access to sensitive information, or even complete system shutdowns. Decentralized AI, however, distributes these functions across a network of independent nodes, each of which operates autonomously yet remains interconnected with the rest of the network. With this decentralized model, there is no single central hub that can be targeted and compromised. Instead, the system's intelligence and processing power are spread out. It makes sure that the failure or compromise of one node does not jeopardize the entire network. This distribution of resources means that even if a cyber attack successfully infiltrates one part of the network, the impact is contained and isolated. The compromised node can be quickly identified and neutralized, while the rest of the network continues to function securely and efficiently. Real-Time Threat Detection and Response Real-Time Threat Detection and Response Traditional AI systems also often face limitations in their ability to swiftly detect and counteract emerging threats. It can create bottlenecks, slowing down the process of identifying, analyzing, and responding to potential cyber-attacks. This delay, later, can give cybercriminals a critical window of opportunity to exploit vulnerabilities before defensive measures are implemented. Meanwhile, decentralized AI leverages its distributed network of nodes to enable faster and more efficient threat detection and response. Each node in a decentralized AI system operates independently, continuously monitoring its environment for suspicious activity or anomalies. When a potential threat is detected by one node, the information is instantly shared across the network, allowing other nodes to adjust their defenses in real-time. Also, decentralized AI's distributed architecture allows it to learn and adapt dynamically as new threats emerge. Each node contributes to a collective intelligence that grows stronger over time. For example, if a particular type of attack is detected in one part of the network, the knowledge and countermeasures developed in response to that attack are immediately shared with all other nodes. Resilience Against Distributed Denial of Service (DDoS) Attacks Resilience Against Distributed Denial of Service (DDoS) Attacks When a DDoS attack happens, cybercriminals flood a network, server, or website with an overwhelming amount of traffic, causing it to become slow, unresponsive, or entirely unavailable to legitimate users and negatively impacting core web vitals . DDoS attack core web vitals These attacks exploit the centralized nature of traditional systems, where all traffic is directed to a single point. This makes it easier for attackers to overload the system and bring it down. Decentralized AI mitigates these risks by distributing data processing and network traffic across a wide array of nodes. Instead of converging on one central server, all traffic is dispersed throughout the network, making it much harder for an attacker to generate enough traffic to disrupt the system. This distributed architecture means that even if a DDoS attack manages to overwhelm a portion of the network, the impact is localized and contained– to prevent the entire system from going offline. The other nodes in the network continue to operate normally and make sure that legitimate users can still access the services and data they need. So, in short: a decentralized structure creates multiple pathways for traffic to flow, which helps maintain service availability even during an attack. Better Collaborative Cybersecurity Efforts Better Collaborative Cybersecurity Efforts Cybersecurity measures are often siloed within individual organizations, with limited sharing of threat intelligence and defense strategies. This lack of collaboration can leave gaps in defenses and make it easier for cybercriminals to exploit vulnerabilities that might go unnoticed by isolated entities. Decentralized AI changes this dynamic by creating a networked environment where nodes, which could represent different organizations or even different sectors, are connected and able to share information in real-time. For example, if one organization’s node detects a new type of malware or identifies a phishing attempt, this information can be immediately communicated across the network. Other nodes can then update their defenses to guard against the same threat, even before they encounter it themselves. An interconnectivity like this allows for a more collective and coordinated approach to cybersecurity, where the experiences and insights gained by one node can benefit the entire network. Adaptive and Self-Healing Capabilities Adaptive and Self-Healing Capabilities The self-healing capability of decentralized AI refers to its ability to automatically repair and recover from disruptions or attacks. We know that in a decentralized system, if a node is compromised or experiences a malfunction, it does not bring down the entire network. Instead, the system can isolate the affected node, mitigate the damage, and allow the other nodes to continue functioning normally. This isolation and repair process is facilitated by the decentralized nature of the network, where each node operates independently but in coordination with others. Let’s say a node is under attack and becomes unresponsive, the network then reroutes traffic and resources to maintain service continuity, while the compromised node is repaired or replaced. Decentralized AI: A Robust Defense Against Cyber Threats Decentralized AI: A Robust Defense Against Cyber Threats Decentralized AI fosters a more dynamic and resilient cybersecurity environment by enabling networks to operate independently yet synergistically. This paradigm shift moves beyond merely fortifying defenses. It encourages a proactive and intelligent approach to security, where systems can autonomously detect, adapt, and recover from threats.

Why Is Decentralized AI a Key to a More Secure Digital World?

MEV (Maximal Extractable Value) is extracting value from a blockchain network by adding, removing, or reordering transactions included in a block. This concept originated in the Ethereum community quite a long time ago (of course, if we count in crypto years). Still, MEV started to reach a new level of volumes with the growth of DeFi infrastructure, which opened more opportunities for servers. In simple words, MEV's working principle is: "Hey, miner, put my transaction next to that one so I earn 1 ETH, and I'll give you half”. Obviously, this is a win-win situation, both for the person who found the transaction (the servers) and for those who can change the order of the transaction when creating the block. An important term when working with MEVs is mempool. In blockchain terminology, a mempool is a waiting pool for transactions that have not yet been added to the block and are still pending. Of course, anyone can see all the transactions that are in this pending state and understand how they can affect certain contract states. What are People Looking for in Blockchain Today At the time of writing, the most popular uses of MEV mechanics right now are. DEX arbitrage and sandwich attacks Mempool provides a unique opportunity for traders to anticipate prices on exchanges. Most decentralized exchanges work with market-making algorithms, which allow knowing the current state of pools (exchange contracts) and incoming transactions from the mempool to know the price state after each upcoming transaction in the block. This opens up the possibility of both normal arbitrage between different exchanges within one block and sandwich attacks— buying and selling around one transaction or vice versa. For example, a trader sees a big transaction in the mempool to buy a SAND token. Then he sends his 2 transactions to the miner: his purchase of SAND token right before the big transaction and instant sale after this transaction. Liquidations Lending protocols such as Maker, Compound, and Aave require users to deposit collateral (e.g., ETH). This deposited collateral is then used to extend credit to other users. Once this collateral has the potential risk of not securing the loan (the risk parameters for this are determined by the DAO community in advance), any network user can liquidate the debt by taking the collateral for themselves. In this case, for example, the debt could be $80k and its collateral $100k, thus the liquidator gets an instant profit. NFT MEV Having access to the general information of incoming transactions makes it possible to detect the completion of NFT auctions or their drops, which opens up opportunities to profit from them. For example, if there is a popular NFT drop and a trader wants a particular NFT or set of NFTs, he can set his transaction so that he is first in line to buy the NFT, or he can buy the entire set of NFTs at once. MEV Infrastructure The Flashbots community played a big role in the development of MEV infrastructure. They proposed the mev-boost infrastructure, which is essentially an improved geth client (i.e., the Ethereum node client). In this article, I will consider this infrastructure, as currently, most builders in the Ethereum network use it. However, the prevalence of Flashbots Builder has fallen dramatically in block production, although at its peak, it collected almost 80% of all Ethereum blocks. Let's briefly walk through the "mining" process in Ethereum 2.0 after the merge. There are now 3 agents responsible for creating blocks: Block builder. Block bidder receives transactions from servers and users who, in addition to paying for gas, express their preferred position in the block by bidding with a capped price. The job of the bidder is to build the most profitable block using various strategies.
Block relay. Block relay is an agent responsible for verifying blocks before they are passed to validators. The block relay protects the validators from spam by checking the builder blocks for validity and evaluating the value of each block associated with the MEV.
Block validator. A block validator in the mev-boost architecture is a Beacon Chain member selected to offer a block for a particular slot. The validator communicates with the relay to obtain the most profitable block header, which it validates by signing it with its public key via escrow. Thus, network users can directly influence the collection of blocks through the mev-boost infrastructure. Bundles are used to organize transactions, which are an ordered set of transactions that the server sends to the builder via API. Practical Example: The Consequences of No MEV Protection Imagine Alice wants to swap a large amount of cryptocurrency on a decentralized exchange (DEX). She submits her transaction to the network, but before it gets included in a block, a malicious actor notices the pending transaction. The actor then submits a series of transactions to manipulate the price of the cryptocurrency, executing a front-running or sandwich attack. Here’s a step-by-step breakdown of what happens: Alice’s transaction submission. Alice submits her transaction to swap 100 ETH for SAND on a DEX.
Pending transaction visibility. Before Alice's transaction is included in a block, it appears in the pending transaction pool, visible to everyone, including malicious actors.
Front-runner's transactions. A malicious actor sees Alice's large transaction and submits two transactions: one to buy ETH just before Alice's transaction (pushing the price up) and one to sell ETH right after (taking advantage of the increased price).
Impact on Alice. By the time Alice's transaction is processed, the price of ETH has increased, meaning she receives fewer SAND for her 100 ETH. The malicious actor profits from the price difference. Without MEV protection, these types of manipulations are more likely to occur, leading to a loss of trust in the blockchain ecosystem and financial harm to users. MEV-Supported RPC Providers Several providers offer RPC nodes with built-in MEV protection, ensuring a safer and fairer blockchain experience. Here are a couple of notable ones. dRPC dRPC provides RPC endpoints with robust MEV protection, prioritizing the fair ordering of transactions and reducing the risk of manipulation. Their services include: Transaction bundling. Ensuring transactions are grouped and ordered in a way that minimizes the potential for MEV extraction.
Privacy features. Concealing pending transactions until they are confirmed, reducing the risk of front-running and sandwich attacks.
Real-time monitoring. Actively scanning for and mitigating potential MEV threats in real-time. dRPC is a leading provider of decentralized blockchain APIs and RPC Nodes, serving clients like Instadapp, SushiSwap, Lido, Curve, and 500+ other web3 organizations. Flashbots Protect Flashbots is a research and development organization focused on mitigating the negative externalities of MEV. Flashbots Protect offers RPC endpoints that help prevent front-running and other MEV-related attacks, promoting a healthier blockchain environment. Their features include: Sealed-bid auction mechanism. Transactions are submitted in a way that hides their details until they are confirmed, preventing pre-execution manipulation.
MEV-resistant mempool. Transactions are managed in a mempool that is designed to be resistant to MEV exploitation.
Collaboration with miners. Working closely with miners to ensure fair transaction ordering and reduce the incentives for MEV extraction. Flashbots primary focus is to enable a permissionless, transparent, and sustainable ecosystem for MEV. Conclusion It is important to choose RPC providers that offer MEV protection. With dRPC and Flashbots Protect, users and developers can significantly reduce the risks associated with MEV and contribute to a more stable and trustworthy decentralized landscape. MEV (Maximal Extractable Value) is extracting value from a blockchain network by adding, removing, or reordering transactions included in a block. This concept originated in the Ethereum community quite a long time ago (of course, if we count in crypto years). Still, MEV started to reach a new level of volumes with the growth of DeFi infrastructure, which opened more opportunities for servers. In simple words, MEV's working principle is: "Hey, miner, put my transaction next to that one so I earn 1 ETH, and I'll give you half”. Obviously, this is a win-win situation, both for the person who found the transaction (the servers) and for those who can change the order of the transaction when creating the block. An important term when working with MEVs is mempool. In blockchain terminology, a mempool is a waiting pool for transactions that have not yet been added to the block and are still pending. Of course, anyone can see all the transactions that are in this pending state and understand how they can affect certain contract states. What are People Looking for in Blockchain Today At the time of writing, the most popular uses of MEV mechanics right now are. DEX arbitrage and sandwich attacks Mempool provides a unique opportunity for traders to anticipate prices on exchanges. Most decentralized exchanges work with market-making algorithms , which allow knowing the current state of pools (exchange contracts) and incoming transactions from the mempool to know the price state after each upcoming transaction in the block. This opens up the possibility of both normal arbitrage between different exchanges within one block and sandwich attacks— buying and selling around one transaction or vice versa. For example, a trader sees a big transaction in the mempool to buy a SAND token. Then he sends his 2 transactions to the miner: his purchase of SAND token right before the big transaction and instant sale after this transaction. market-making algorithms Liquidations Lending protocols such as Maker, Compound, and Aave require users to deposit collateral (e.g., ETH). This deposited collateral is then used to extend credit to other users. Once this collateral has the potential risk of not securing the loan (the risk parameters for this are determined by the DAO community in advance), any network user can liquidate the debt by taking the collateral for themselves. In this case, for example, the debt could be $80k and its collateral $100k, thus the liquidator gets an instant profit. NFT MEV Having access to the general information of incoming transactions makes it possible to detect the completion of NFT auctions or their drops, which opens up opportunities to profit from them. For example, if there is a popular NFT drop and a trader wants a particular NFT or set of NFTs, he can set his transaction so that he is first in line to buy the NFT, or he can buy the entire set of NFTs at once. MEV Infrastructure The Flashbots community played a big role in the development of MEV infrastructure. They proposed the mev-boost infrastructure, which is essentially an improved geth client (i.e., the Ethereum node client). In this article, I will consider this infrastructure, as currently, most builders in the Ethereum network use it. However, the prevalence of Flashbots Builder has fallen dramatically in block production, although at its peak, it collected almost 80% of all Ethereum blocks. Let's briefly walk through the "mining" process in Ethereum 2.0 after the merge. There are now 3 agents responsible for creating blocks: Block builder. Block bidder receives transactions from servers and users who, in addition to paying for gas, express their preferred position in the block by bidding with a capped price. The job of the bidder is to build the most profitable block using various strategies. Block relay. Block relay is an agent responsible for verifying blocks before they are passed to validators. The block relay protects the validators from spam by checking the builder blocks for validity and evaluating the value of each block associated with the MEV. Block validator. A block validator in the mev-boost architecture is a Beacon Chain member selected to offer a block for a particular slot. The validator communicates with the relay to obtain the most profitable block header, which it validates by signing it with its public key via escrow. Block builder. Block bidder receives transactions from servers and users who, in addition to paying for gas, express their preferred position in the block by bidding with a capped price. The job of the bidder is to build the most profitable block using various strategies. Block relay. Block relay is an agent responsible for verifying blocks before they are passed to validators. The block relay protects the validators from spam by checking the builder blocks for validity and evaluating the value of each block associated with the MEV. Block validator. A block validator in the mev-boost architecture is a Beacon Chain member selected to offer a block for a particular slot. The validator communicates with the relay to obtain the most profitable block header, which it validates by signing it with its public key via escrow. Thus, network users can directly influence the collection of blocks through the mev-boost infrastructure. Bundles are used to organize transactions, which are an ordered set of transactions that the server sends to the builder via API. Practical Example: The Consequences of No MEV Protection Imagine Alice wants to swap a large amount of cryptocurrency on a decentralized exchange (DEX). She submits her transaction to the network, but before it gets included in a block, a malicious actor notices the pending transaction. The actor then submits a series of transactions to manipulate the price of the cryptocurrency, executing a front-running or sandwich attack. Here’s a step-by-step breakdown of what happens: Alice’s transaction submission. Alice submits her transaction to swap 100 ETH for SAND on a DEX. Pending transaction visibility. Before Alice's transaction is included in a block, it appears in the pending transaction pool, visible to everyone, including malicious actors. Front-runner's transactions. A malicious actor sees Alice's large transaction and submits two transactions: one to buy ETH just before Alice's transaction (pushing the price up) and one to sell ETH right after (taking advantage of the increased price). Impact on Alice. By the time Alice's transaction is processed, the price of ETH has increased, meaning she receives fewer SAND for her 100 ETH. The malicious actor profits from the price difference. Alice’s transaction submission. Alice submits her transaction to swap 100 ETH for SAND on a DEX. Pending transaction visibility. Before Alice's transaction is included in a block, it appears in the pending transaction pool, visible to everyone, including malicious actors. Front-runner's transactions. A malicious actor sees Alice's large transaction and submits two transactions: one to buy ETH just before Alice's transaction (pushing the price up) and one to sell ETH right after (taking advantage of the increased price). Impact on Alice. By the time Alice's transaction is processed, the price of ETH has increased, meaning she receives fewer SAND for her 100 ETH. The malicious actor profits from the price difference. Without MEV protection, these types of manipulations are more likely to occur, leading to a loss of trust in the blockchain ecosystem and financial harm to users. MEV-Supported RPC Providers Several providers offer RPC nodes with built-in MEV protection, ensuring a safer and fairer blockchain experience. Here are a couple of notable ones. dRPC dRPC provides RPC endpoints with robust MEV protection, prioritizing the fair ordering of transactions and reducing the risk of manipulation. Their services include: Transaction bundling. Ensuring transactions are grouped and ordered in a way that minimizes the potential for MEV extraction. Privacy features. Concealing pending transactions until they are confirmed, reducing the risk of front-running and sandwich attacks. Real-time monitoring. Actively scanning for and mitigating potential MEV threats in real-time. Transaction bundling. Ensuring transactions are grouped and ordered in a way that minimizes the potential for MEV extraction. Privacy features. Concealing pending transactions until they are confirmed, reducing the risk of front-running and sandwich attacks. Real-time monitoring. Actively scanning for and mitigating potential MEV threats in real-time. dRPC is a leading provider of decentralized blockchain APIs and RPC Nodes, serving clients like Instadapp, SushiSwap, Lido, Curve, and 500+ other web3 organizations. Flashbots Protect Flashbots is a research and development organization focused on mitigating the negative externalities of MEV. Flashbots Protect offers RPC endpoints that help prevent front-running and other MEV-related attacks, promoting a healthier blockchain environment. Their features include: Sealed-bid auction mechanism. Transactions are submitted in a way that hides their details until they are confirmed, preventing pre-execution manipulation. MEV-resistant mempool. Transactions are managed in a mempool that is designed to be resistant to MEV exploitation. Collaboration with miners. Working closely with miners to ensure fair transaction ordering and reduce the incentives for MEV extraction. Sealed-bid auction mechanism. Transactions are submitted in a way that hides their details until they are confirmed, preventing pre-execution manipulation. MEV-resistant mempool. Transactions are managed in a mempool that is designed to be resistant to MEV exploitation. Collaboration with miners. Working closely with miners to ensure fair transaction ordering and reduce the incentives for MEV extraction. Flashbots primary focus is to enable a permissionless , transparent , and sustainable ecosystem for MEV. permissionless transparent sustainable Conclusion It is important to choose RPC providers that offer MEV protection. With dRPC and Flashbots Protect, users and developers can significantly reduce the risks associated with MEV and contribute to a more stable and trustworthy decentralized landscape.

Why Is MEV Protection Crucial for RPC nodes?

কেন MEV সুরক্ষা RPC নোডের জন্য গুরুত্বপূর্ণ?

Warum ist MEV-Schutz für RPC-Knoten von entscheidender Bedeutung?

¿Por qué la protección MEV es crucial para los nodos RPC?

Pourquoi la protection MEV est-elle cruciale pour les nœuds RPC ?

RPC नोड्स के लिए MEV सुरक्षा महत्वपूर्ण क्यों है?

MEV 保護が RPC ノードにとってなぜ重要なのか?

RPC 노드에 MEV 보호가 중요한 이유는 무엇입니까?

Por que a proteção MEV é crucial para nós RPC?

Почему защита MEV имеет решающее значение для узлов RPC?

MEV Koruması RPC Düğümleri İçin Neden Önemlidir?

Tại sao bảo vệ MEV lại quan trọng đối với các nút RPC?

为什么 MEV 保护对 RPC 节点至关重要？

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