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Hot takes on artificial intelligence are everywhere: Depending on where you look, generative AI will either kill us, take our jobs, liberate us from drudgery, or spur tremendous innovation. (Either way, calls for are here.) At The Markup we like to take a measured approach that often, well, includes measuring actual consequences. regulation So I’m thrilled to share this Q&A with associate professor of computer science at University of California, Riverside, , who together with his team—Riverside Ph.D. candidates and , and , an associate professor of computer science at University of Texas at Arlington—recently published a paper quantifying the . Shaolei Ren Pengfei Li Jianyi Yang Mohammad A. Islam secret water footprint of AI While the carbon footprint of emerging technologies has gotten some attention, to truly build toward sustainability, water has to be part of the equation too. Also, hello! I’m Nabiha Syed, the CEO of The Markup, and I happen to be a person who believes technological advancement can coexist with a healthy planet—if we make that a priority. Keep reading to learn how much water a single ChatGPT conversation might “drink” and tangible steps we can take to reduce the water footprint of AI. (This Q&A has been edited for brevity and clarity.) Syed: With very good reason, we’re starting to see more scrutiny of the carbon footprint of various technologies, including AI models like GPT‑3 and GPT‑4 as well as . But your research focuses on something receiving less attention: the secret water footprint of AI technology. Tell us about your findings. bitcoin mining Water footprint has been staying under the radar for various reasons, including the big misperception that freshwater is an “infinite” resource and the relatively lower price tag of water. Many AI model developers are not even aware of their water footprint. But this doesn’t mean water footprint is not important, especially in drought regions like California. Ren: Together with my students and my collaborator at UT Arlington, I did some research on AI’s water footprint using state-of-the-art estimation methodology. We find that large-scale AI models are indeed big water consumers. For example, training GPT‑3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough to produce 370 BMW cars or 320 Tesla electric vehicles), and the water consumption would have been tripled if training were done in Microsoft’s data centers in Asia. These numbers do not include the off-site water footprint associated with electricity generation. For inference (i.e., conversation with ChatGPT), our estimate shows that ChatGPT needs a 500-ml bottle of water for a short conversation of roughly 20 to 50 questions and answers, depending on when and where the model is deployed. Given ChatGPT’s huge user base, the total water footprint for inference can be enormous. Then, we further studied the unique spatial-temporal diversities of AI models’ runtime water efficiency—the water efficiency changes over time and over locations. This implies that there’s potential to reduce AI’s water footprint by dynamically scheduling AI workloads and tasks at certain times and in certain locations, the way we reduce our electricity bills by utilizing the low electricity prices during the night to charge our electric vehicles. Syed: How does this water footprint compare to something like, say, beef? We might see from some websites that beef and jeans have a big water footprint, but their water footprint is for the entire life cycle and includes a large portion of nonpotable water. For example, the water footprint of jeans starts from cotton growth. Ren: In our study, we only consider the operational water footprint (i.e., water consumption associated with training and running the AI models), whereas the embodied water footprint (e.g., water footprint associated with AI server manufacturing and transportation, including chipmaking) is excluded. If we factor in the embodied water footprint for AI models, my gut feeling is that the overall water footprint would be easily increased by 10 times or even more. Syed: I’m particularly curious about how carbon reduction and water conservation might be in tension with one another. What do you mean when you ask whether we should “follow or unfollow the sun”? Water efficiency mostly depends on the outside temperature as well as energy fuel mixes for electricity generation. Carbon-efficient hours/locations do not mean water-efficient hours/locations, and sometimes they’re even opposite to each other. Ren: Water-conscious users may prefer to use the inference services of AI models during water-efficient hours and/or in water-efficient data centers…. For example, in California, there’s a high solar energy production around noon, and this leads to the most carbon-efficient hours, but around noon, the outside temperature is also high, and hence the water efficiency is the worst. As a result, if we only consider carbon footprint reduction (say, by scheduling more AI training around noon), we’ll likely end up with higher water consumption, which is not truly sustainable for AI. On the other hand, if we only reduce water footprint (say, by scheduling AI training at midnight), we may increase the carbon footprint due to less solar energy available. Syed: It’s clear that tech giants like Microsoft, Google, and Amazon are betting big on the future of AI, but are we seeing them make environmental considerations a priority in their development? Yes, we’ve been seeing water footprint emerge as a priority in the sustainability reports of several tech giants such as , , and . Ren: Google Microsoft Meta Also, legislators have recently begun to consider the impact of data centers’ water usage on the local environment. For example, in Virginia, where Loudon County is known as the “data center capital of the world,” , proposed early this year, would require “a site assessment … to examine the effect of the data center on water usage and carbon emissions as well as any impacts on agricultural resources.” SB 1078 Most of the industry’s efforts so far have been focused on improving the on-site water efficiency from the “engineering” perspective, e.g., improving the data center’s cooling tower efficiency and processing recycled water instead of tapping into the local potable water resources. Nonetheless, the vast majority of data centers still use potable water and cooling towers. For example, even tech giants such as Google heavily rely on cooling towers and consume billions of liters of potable water each year. Such huge water consumption has produced a stress on the local water infrastructure; Google’s data center used in The Dalles, Ore. Moreover, many data centers are also located in drought-prone areas such as California. more than a quarter of all the water Our study shows that “when” and “where” to train a large AI model can significantly affect the water footprint. The underlying reason is the spatial-temporal diversity of both on-site and off-site water usage effectiveness (WUE)—on-site WUE changes due to variations of outside weather conditions, and off-site WUE changes due to variations of the grid’s energy fuel mixes to meet time-varying demands. In fact, WUE varies at a much faster timescale than monthly or seasonably. Therefore, by exploiting spatial-temporal diversity of WUE, we can dynamically schedule AI model training and inference to cut the water footprint. For example, if we train a small AI model, we can schedule the training task at midnight and/or in a data center location with better water efficiency. Likewise, some water-conscious users may prefer to use the inference services of AI models during water-efficient hours and/or in water-efficient data centers, which can contribute to the reduction of AI models’ water footprint for inference. Such demand-side water management complements the existing engineering-based on-site water-saving approaches that focus on the supply side. Also, our approach is software-based and hence can be used together with any cooling systems for free without particular requirements on the climate conditions or new cooling system installations. By having more transparency, we’d be able to know exactly when and where we have the most water-efficient AI models. Syed: You propose transparency as a helpful next step. What questions could greater transparency help us answer? By having more transparency, we’d be able to know exactly when and where we have the most water-efficient AI models. Ren: Transparency also makes it possible to measure, benchmark, and improve the AI models’ water footprint, which can be of great value to the research community. Currently, some AI conferences have requested that authors declare their AI models’ carbon footprint in their papers; we believe that with transparency and awareness, authors can also declare their AI models’ water footprint as part of the environmental impact. With such information, AI model developers can better schedule their AI model training and also exploit the spatial-temporal diversity to better route the users’ inference requests to save water with little to zero degradation in other performance metrics. Additionally, transparency can also let users know their water footprint at runtime and better their water footprint (say, they might want to defer some nonurgent inference requests to water-efficient hours if possible). Apple has integrated clean energy scheduling into its iPhone products by selecting low-carbon hours for charging, and we hope that water-aware AI training and inference can also be turned into reality in the future. I hope you found this as thought-provoking as I did! This one hit especially close to home: My dad and sister are both water resources engineers, and I grew up in drought-riddled Southern California. We’re in a technological boom, for sure—but innovation should serve our public good, not obliterate it. Thanks for reading! Always, Nabiha Syed Chief Executive Officer The Markup By Nabiha Syed Also published here Photo by on David Becker Unsplash

AI Tech and Its Secret Water Footprint 

এআই টেক এবং এর সিক্রেট ওয়াটার ফুটপ্রিন্ট

KI-Technologie und ihr geheimer Wasser-Fußabdruck

La tecnología de inteligencia artificial y su huella hídrica secreta

La technologie IA et son empreinte hydrique secrète

एआई टेक और इसका गुप्त जल पदचिह्न

AI テクノロジーとその秘密の水のフットプリント

AI 기술과 그 비밀의 물 발자국

AI Tech e sua pegada hídrica secreta

Технологии искусственного интеллекта и их секретный водный след

Yapay Zeka Teknolojisi ve Gizli Su Ayak İzi

Công nghệ AI và dấu chân nước bí mật của nó

人工智能技术及其秘密水足迹

Overview Digital fingerprinting is a technique used to identify users across different websites based on their unique device and browser characteristics. These characteristics - fingerprint parameters, can include various software, hardware (CPU, RAM, GPU, media devices - cameras, mics, speakers), location, time zone, IP, screen size/resolution, browser/OS languages, network, internet provider-related and other attributes. The combination of these parameters creates a unique identifier - fingerprint, that can be used to track a user's online activity. Fingerprints play a crucial role in online security, enabling services to identify and authenticate unique users. They also make it possible for users to trick such systems to stay anonymous online. However, if you can manipulate your fingerprints, you can run tens or hundreds or more different accounts to pretend that they are unique, authentic users. While this may sound cool, it has serious implications as it can make it possible to create an army of bots that can spread spam and fakes all over the internet, potentially resulting in fraudulent actions. Note: Obviously, I won’t discuss here how you can do "bad" things; you must always be careful, stay away from the “dark side,” and avoid committing illegal actions. This article is about the technology behind this, so use the information wisely. This is the first (and maybe not the last) article in this domain, so it will be more like an overview. The content offers an intro to the topic. There are dozens of tools and ways to detect, collect, and spoof fingerprints, and there are many different parameters and technologies that can reveal or hide your real identity or the fact that you're spoofing fingerprints (meaning you're not an authentic user). Parameters in Digital Fingerprints Let's consider some of them, some examples of obvious, simple, or well-known params, some that are rarer, less-know, and difficult to spoof. this string provides information about the user's browser, operating system, and device. User-Agent: it reveals the user's network and geo-location. Services use IP addresses for security and to prevent malicious activities. If you use one account from different IPs or many different accounts from the same IP, some services may view this activity (in combination with other params) as suspicious and use some level of bot protection against you. This can also be triggered if you use IPs that are already used or in some kind of banned list (proxies). IP: Information about installed plugins and extensions can be used to create a unique fingerprint - it helps in identifying users based on their browser's additional functionalities. Browser Plugins and Extensions: different users often have different display characteristics. Screen Resolution and Color Depth: they are important factors in fingerprinting - if you have random values, e.g., Japan timezone Norwegian language this is a bit unusual. Timezone and Language Settings: it involves rendering hidden graphics in the user's browser to gather information about the graphics hardware. Quite a tricky one because if you spoof your hardware info, then you can’t have the proper canvas value - you can’t render it on your different real hardware. Canvas: it exploits the unique capabilities and limitations of the user's graphics hardware in rendering 3D graphics - additional information about the user's device. WebGL Fingerprinting: the list of fonts installed on a user's system can be used as a fingerprinting parameter. This information is accessible through JavaScript. The list of fonts has to be realistic - so e.g. you can’t have Win fonts on macOS or you can’t have just 1 or 2 fonts, this is a very suspicious indicator. Fonts: it allows websites to determine the device's battery level and charging status, the combination of battery attributes can be used for fingerprinting. Battery status API: websites can use the web audio API to generate unique audio fingerprints by analyzing the audio processing characteristics of the device. Audio Fingerprinting: This information includes details about the software and hardware components of the device, such as the graphics card, network adapter, and operating system. This adds to the uniqueness of the fingerprint. Hardware Concurrency: This information includes details about the network connection, such as the IP address, ISP, and DNS server. Network Information: some users may use some software that listens to specific ports which also might be useful for some specific cases. Open and used ports: Navigating the Web incognito Digital fingerprint manipulation is a difficult task that demands a proactive approach to avoid detection. The are lots of strategies and tools to hide your identity (fingerprints) or pretend that you are someone else. Use privacy-focused browsers Opting for privacy-focused browsers, such as Brave, Ghostery, Tor, Octo Browser, or Vivaldi with enhanced privacy settings, provides a fundamental defense against common fingerprinting techniques. These browsers prioritize user privacy and incorporate features designed to hide your real fingerprints, making it harder to track your activities. For example, the Tor Browser, grounded in principles of anonymity, routes internet traffic through the Tor network. This strategic routing obscures the user's identity by bouncing connections through a series of volunteer-operated servers, enhancing overall online anonymity. Browser Extensions Privacy-centric browser extensions, including AdBlock, uBlock Origin, Privacy Badger, or CanvasBlocker, could be active defenses against tracking scripts, cookies, and fingerprinting attempts. These tools operate in the background, protecting and preserving user anonymity. VPN and Proxies The use of VPNs or proxy services adds an additional layer of protection by masking the user's real IPs (location, timezone, language, etc) and encrypting internet traffic. This is not only used to change your fingerprints but also to provide a more anonymous online presence and a higher security level. Motivations behind fingerprint spoofing Understanding the Whys Individuals concerned about personal privacy and the growing digital surveillance often spoof fingerprints to shield themselves from relentless online tracking. Privacy: Spoofing fingerprints prove useful for circumventing geo-restrictions, allowing users to access content restricted to specific regions. VPN and proxy services play a crucial role here by not only hiding the user's identity but also providing access to servers in different geographic locations. Geo-restriction: Avoiding targeted advertising and online profiling serves as a compelling motive for individuals to manipulate their digital fingerprints. Some tools can block 3rd-party tracking scripts and cookies, disrupting the profiling process. Ads: Web scraping, multi-accounting, e-commerce, bounty&airdrop, bonus-hunting, social networks bots, and affiliate marketing are often sources of income or bases for medium businesses. Digital agencies, individuals, and influencers engaged in these activities may require fingerprint spoofing to navigate through complicated bot detection systems. Avoiding detection becomes vital for some services, ensuring that legitimate activities are not mistakenly flagged or restricted (though quite often, such activities and accounts are not really legit but pretend to be so). Some of the most popular tools are Multilogin, X-Browser, Octo Browser, AdsPower, Incogniton, Scrapy, Surfsky, Web Scraper.io, ScrappingBee, etc. Strategic needs in the digital arena, bot detection, and protection systems: Collecting Users' Fingerprints Websites use JS (obviously) to harvest details about users to construct comprehensive digital portraits. This involves probing screen resolution, device orientation, mouse movements, keystroke dynamics, etc. Sophisticated fingerprinting scripts enumerate a wide range of browser and hardware attributes. JavaScript: Persistent cookies and data stored in local storage work to track users across sessions and platforms. Techniques include leveraging browser cookies and storing unique identifiers for user tracking. Depending on your purposes you need to know when you need an "empty" browser with unique fingerprints or when you need to trick a service showing that you have particular cookies and the same fingerprints. Cookies and local storage: The inclusion of 3rd-party scripts for analytics and advertising embeds invisible trackers often used to adjust systems behavior for a specific user based on collected fingerprints. You can use the aforementioned tools and approaches to selectively block or trick such scripts to get the needed behavior. 3rd-party scripts: Understanding Your Online Identity Panopticlick (EFF), Pixelscan, deviceinfo.me, and BrowserLeaks show your browser's fingerprint parameters such as User-Agent, canvas fingerprinting, fonts, etc. These checkers provide insights into the uniqueness and stability of your digital fingerprint across different browsing sessions. Online checkers: they allow users to inspect network requests, cookies, and other fingerprinting parameters, fostering a deeper understanding of their digital imprint and how websites detect bots and unique legit users and collect fingerprints. Examining the Network and Application tabs in browsers provides a real-time view of the data exchanged between the browser, websites, and servers. Browser developer tools: Here are some examples of how you can get some info about users' fingerprints in the browser. GEO: navigator.geolocation.getCurrentPosition(function(position) { var userLocation = position.coords; }); Microphone: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var microphones = devices.filter(device => device.kind === 'audioinput'); }); Camera: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var cameras = devices.filter(device => device.kind === 'videoinput'); }); Speakers: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var speakers = devices.filter(device => device.kind === 'audiooutput'); }); Audio: var audioContext = new (window.AudioContext || window.webkitAudioContext)(); var oscillator = audioContext.createOscillator(); var analyser = audioContext.createAnalyser(); oscillator.connect(analyser); analyser.connect(audioContext.destination); GPU: var canvas = document.createElement('canvas'); var gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl'); var renderer = gl.getParameter(gl.RENDERER); console.log(renderer); Fonts: var fonts = []; var fontList = document.fonts.forEach(function(font) { fonts.push(font.family); }); console.log(fonts); Canvas (hashing): var canvas = document.createElement('canvas'); var context = canvas.getContext('2d'); var dataURL = canvas.toDataURL(); var canvasHash = MurmurHash3(dataURL); console.log(canvasHash); RAM and CPU: function getCPUInfo() { return navigator.hardwareConcurrency || 0; } function getDeviceMemory() { return navigator.deviceMemory || 0; } var cpuInfo = getCPUInfo(); var deviceMemory = getDeviceMemory(); console.log(`CPU Cores: ${cpuInfo}`); console.log(`Device Memory (GB): ${deviceMemory}`); Bot Detection Systems: Defending Against Automation A couple of examples of such systems: It is a global leader in bot detection and mitigation. It provides a proactive and precise way to verify that legitimate human users are accessing your website, mobile app, and APIs. Distil Networks uses a combination of techniques to detect bots. It studies a range of variables such as cursor movement, click patterns, and web browsing patterns across other websites. It uses , which checks all visitors against hundreds of different characteristics, focusing on their behavior. Distil Networks defends against web scraping, competitive data mining, account takeovers, transaction fraud, unauthorized vulnerability scans, spam, click fraud, denial of service, and API abuse. It can automatically block 99.9% of malicious traffic without impacting legitimate users. It also offers Distil Bot Defense for Web and API, which protect your website and API servers respectively. Distil Networks: Bot Detection: device fingerprints and Are You a Human technology It actively pulls additional data from the browser to identify devices with precision. When a browser request comes in, Distil interrogates the headers to see if the visitor is lying about their identity. This unique identifier can be used to fully or partially identify individual devices even when cookies cannot be read or stored in the browser, the client IP address is hidden, or one switches to another browser on the same device. It uses machine learning to detect biometric patterns in mouse activity and scrolling, as well as looking at browsers, devices, and other factors for clues. It can detect many simple or medium complexity bots that run JS from a web page. Resource Protection: It's a comprehensive cybersecurity platform that integrates fingerprint-based identification to discern legitimate users from potential threats. Utilizing advanced behavioral analysis and anomaly detection, Imperva builds profiles based on a combination of factors, including IP reputation, user-agent, different fingerprint params, and behavioral characteristics. This allows the system to detect suspicious activities indicative of bot traffic and mitigate potential threats effectively. Imperva: It's a prominent content delivery and cloud service provider that incorporates robust bot detection mechanisms within its security offerings. Leveraging a combination of fingerprint-based detection, behavior analysis, and machine learning, Akamai identifies and mitigates various bots. Akamai's global network allows for real-time threat intelligence, enabling proactive defense against evolving bot tactics. Akamai: It's a widely used content delivery network and security service that employs a multifaceted approach to bot detection. Cloudflare distinguishes between human users and bots by analyzing parameters such as IP reputation, user-agent characteristics, and behavioral patterns. The platform also utilizes threat intelligence and community-driven insights to stay ahead in the battle against emerging bot threats. Cloudflare uses to profile SSL/TLS clients and block potential bot requests. It also uses to apply rules and route traffic based on HTTP request information. is used to identify specific files and prevent data loss. These techniques help in detecting not real users. Cloudflare: JA3 Fingerprinting HTTP Filtering Data Fingerprinting Role of Bot Detection Systems: They play a pivotal role in enhancing bot detection capabilities. With servers strategically positioned around the globe, they can leverage geographical insights and real-time threat intelligence to identify and mitigate bot traffic effectively. The platform's WAF and bot managers contribute to a comprehensive defense against automated "users.” In this ongoing cat-and-mouse game, Bot Detection Systems continue to evolve, leveraging advanced technologies to stay ahead of fingerprint spoofing tech. The collaboration between security providers, businesses, and the wider online community remains crucial in defense against the tactics of automated threats but also, in a way, used against users' privacy, anonymity, and experience online, bringing intrusive ads and sophisticated user tracking tools that are only beneficial for businesses not for users. However, privacy protection, fingerprint hiding and spoofing, and ads and trackers blocking tools are also developing to evade even the most sophisticated user tracking systems. Websites' additional verification steps If a fingerprint appears suspicious, such as frequent changes or unusual combinations of data points, the website may trigger additional verification steps. Suspicious behavior: Websites may present users with captcha challenges to verify their identity. This involves solving puzzles or identifying distorted text, tasks that are difficult to perform for primitive bots. Captcha: Websites may request users to verify their mobile phone numbers to establish a stronger connection between a unique identity and a real-world entity. This can help prevent fraudulent activities. Mobile verification: the idea is the same as with mobile phones, but you need to submit your ID ID verification: Examples: Google doesn't require additional verification for new users with clear, consistent fingerprints. If a user's fingerprint changes frequently or resembles those used by known bots (or just already known for Google), Google may ask for a mobile phone number or ask to deal with a captcha. Financial websites often enforce stricter verification measures due to the sensitive nature of transactions. They may require users to provide additional personal information, verify their identity through secure channels, or pass more complex captcha challenges. Opinion: spoofing is better than hiding Hiding fingerprints can enhance privacy, security, and online anonymity, but spoofing them to appear as a unique, legitimate user offers even greater advantages. By blending in with the crowd and avoiding suspicion from systems that detect anti-detection tools, you can maintain credibility and trust. This approach allows you to enjoy the benefits of being perceived as a unique, legitimate user, minimizing the chance of facing additional protecting measures or obstacles. and, obviously, it helps in preventing leaking of your real data and identity. Also published . here

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Storing passwords can be a nuance due to the liability of them being compromised. To make matters worse, users tend to reuse passwords across services which make storing them securely even more important. The aim behind storing passwords securely is that even if the database containing them is compromised, the attacker can’t decipher any user’s actual password. This rules out storing passwords in . plain text Using encryption may seem to be a good choice since the attacker would not know the actual passwords (because they are encrypted). However, if the database is compromised, then the encryption keys would probably be compromised as well. Using these keys, the attacker would be able to decrypt the encrypted passwords - making this method of storage weak. This is where hashing or hash functions come into play. What are hash functions? They are functions that have these properties: : Given the output of a hash function , it should be hard to find any input , which when hashed, results in the same output ( ). For example, if I take a random SHA256 hash output ( datatype) like , it should be very hard to know what the input to the hash function was that resulted in this output. Try googling your way into finding it! Preimage resistance Out In hash(In) = Out string "401357cf18542b4117ca59800657b64cce2a36d8ad4c56b6102a1e0b03049e97" : If the input to a hash function is known, it should be hard to find another input that has the same hashed output. Second preimage resistance : This says that it is hard to find any two inputs such that their hashed output is the same. This is slightly different from (2) since in (2), you are given one input, and in this case, you can cook up any input. Collision resistance : The hash function should always return the same output given the same input. Predictability : The output of the hash function always has the same length (number of chars), regardless of the input’s length. Fixed Length Output : A small change in the input (even just one character), should have a large change in the output string. For example, the SHA256 hash of is . But the hash of is . As you can see, the outputs are verified differently. Input sensitivity "hello" "2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824" "hella" "70de66401b1399d79b843521ee726dcec1e9a8cb5708ec1520f1f3bb4b1dd984" All in all, hash functions are “one-way functions”. If you only know the output, it’s impossible/difficult to know its input. This is unlike encryption in which given the output and the encryption key, you can know the input. Due to this one-way property, storing a hashed value of a password is a good idea since if their hash is compromised (via a database leak), the attacker would not know the original password (which is the input to the hash function). In fact, the only “entity” which would know the input to the hash function would be the end-user who generated the password in the first place. This is exactly what we want from a security point of view. Due to property number (5), there are an infinite number of inputs that can yield the same hash output. But it’s also very difficult to find any one of those infinite numbers of inputs given a specific output! What is salting and why hashing alone is not good enough - Problems with humans Many people tend to use like "password", "12345" etc. Since the hash of the same input never changes (see property (4) above), we can precompute the hash of common passwords and then check the leaked database data against those precomputed hashes. common passwords For example, the SHA256 hash of is . If this hash is seen in a database, we know that the user’s password is . In fact, there is an entire database of precomputed hashes that can be checked against. These are called . "12345" "5994471abb01112afcc18159f6cc74b4f511b99806da59b3caf5a9c173cacfc5" "12345" rainbow tables The way to solve this problem is to add some random string, known as “salt”, to a password before hashing it (during the signup process), and then we append that random string to the computed hash before storing it in the database. Let’s take an example: Alice’s password: "12345" Bob’s password: "12345" Alice’s random string (salt): "ab$45" Bob’s random string (salt): "ih&g3" Alice’s modified password: "12345ab$45" Bob’s modified password: "12345ih&g3" Alice’s SHA256 hash: "2bb12bb768eb669f0e4b9df29e22a00467eb513c275ccfff1013288facac7889" Bob’s SHA256 hash: "b63400702c6f012aeaa57b5dc7eefaaaf3207cc6b68917911c410015ac0659b2" As you can see, their computed hashes are completely different, even though their passwords are the same. Most importantly, since they are generated from fairly random strings ( and ). these hashes won’t be in any rainbow table "12345ab$45" "12345ih&g3" Before we store these hashes in the database, we must append the salt to them: Alice’s hash + salt: "2bb12bb768eb669f0e4b9df29e22a00467eb513c275ccfff1013288facac7889.ab$45" Bob’s hash + salt: "b63400702c6f012aeaa57b5dc7eefaaaf3207cc6b68917911c410015ac0659b2.ih&g3" The reason we append the salt to the hash is so that during the verification process, we have to use the same salt as we did originally. So we must store this somewhere. Even if the salt is compromised, it’s not a security issue since the attacker would still need to know/guess the user’s password to be able to generate the same hash. Let’s have a look at how the verification process happens: Alice’s password: (Incorrect password) "abcdef" Alice’s salt: (fetched from the db) "ab$45" Alice’s modified password: "abcdefab$45" Alice’s SHA256 hash: "c5110931a3ae4762c1c0334d8eeba8c9c555962cf7d2750fdd732936319a058c" Alice’s hash + salt: "c5110931a3ae4762c1c0334d8eeba8c9c555962cf7d2750fdd732936319a058c.ab$45" Since the computed hash + salt doesn’t match the one in the database, we reject this password. If Alice was to enter her correct password ( ), it would indeed generate the same hash + salt as the one in the database, verifying her identity. "12345" Which hash function to choose? Even after salting, the issue of brute force attacks still remains. An attacker could repeatedly guess different passwords (very quickly) to see which one matches the leaked hash. There are two dimensions that determine how quickly an attacker can find a match: The randomness and length of the user’s password. The time it takes for the hash function to compute the hash If a user uses a random and long enough password, the chances of the attacker guessing that exact string reduces. This means they have to crunch through more guesses which will take more time. that estimates how much time it would take to guess a given password. Here is a really cool tool The slower and more computationally expensive the hashing function, the more time it would take to validate each guess. As of this writing (2nd March 2022), the recommended hashing technique is to use with a minimum configuration of 15 MiB of memory, an iteration count of 2, and 1 degree of parallelism. Argon2id As computational power increases, recommended hashing techniques change as well. Even if the algorithm remains the same, the recommended number of “rounds”  / amount of “work” that should be done per password hash may increase. Example code NodeJS import * as argon2 from "argon2";
import * as crypto from "crypto";
 
const hashingConfig = { // based on OWASP cheat sheet recommendations (as of March, 2022)
    parallelism: 1,
    memoryCost: 64000, // 64 mb
    timeCost: 3 // number of itetations
}
 
async function hashPassword(password: string) {
    let salt = crypto.randomBytes(16);
    return await argon2.hash(password, {
        ...hashingConfig,
        salt,
    })
}
 
async function verifyPasswordWithHash(password: string, hash: string) {
    return await argon2.verify(hash, password, hashingConfig);
}
 
hashPassword("somePassword").then(async (hash) => {
    console.log("Hash + salt of the password:", hash)
    console.log("Password verification success:", await verifyPasswordWithHash("somePassword", hash));
}); The above produces the following output: Hash + salt of the password: $argon2i$v=19$m=15000,t=3,p=1$tgSmiYOCjQ0im5U6NXEvPg$xKC4V31JqIK2XO91fnMCfevATq1rVDjIRX0cf/dnbKY
 
Password verification success: true If you run the above program, it will produce a different hash each time since the salt is regenerated each time. GoLang package main
 
import (
    "crypto/rand"
    "crypto/subtle"
    "encoding/base64"
    "errors"
    "fmt"
    "log"
    "strings"

    "golang.org/x/crypto/argon2"
)
 
type params struct {
    memory      uint32
    iterations  uint32
    parallelism uint8
    saltLength  uint32
    keyLength   uint32
}
 
func main() {
    p := &params{
        memory:      64 * 1024, // 64 MB
        iterations:  3,
        parallelism: 1,
        saltLength:  16,
        keyLength:   32,
    }

    encodedHash, err := generateHashFromPassword("somePassword", p)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println("Hash + salt of the password:")
    fmt.Println(encodedHash)

    match, err := verifyPassword("somePassword", encodedHash)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("\nPassword verification success: %v\n", match)
}
 
func generateHashFromPassword(password string, p *params) (encodedHash string, err error) {
    salt, err := generateRandomBytes(p.saltLength)
    if err != nil {
        return "", err
    }

    hash := argon2.IDKey([]byte(password), salt, p.iterations, p.memory, p.parallelism, p.keyLength)

    // Base64 encode the salt and hashed password.
    b64Salt := base64.RawStdEncoding.EncodeToString(salt)
    b64Hash := base64.RawStdEncoding.EncodeToString(hash)

    // Return a string using the standard encoded hash representation.
    encodedHash = fmt.Sprintf("$argon2id$v=%d$m=%d,t=%d,p=%d$%s$%s", argon2.Version, p.memory, p.iterations, p.parallelism, b64Salt, b64Hash)

    return encodedHash, nil
}
 
func generateRandomBytes(n uint32) ([]byte, error) {
    b := make([]byte, n)
    _, err := rand.Read(b)
    if err != nil {
        return nil, err
    }

    return b, nil
}
 
func verifyPassword(password, encodedHash string) (match bool, err error) {
    // Extract the parameters, salt and derived key from the encoded password
    // hash.
    p, salt, hash, err := decodeHash(encodedHash)
    if err != nil {
        return false, err
    }

    // Derive the key from the other password using the same parameters.
    otherHash := argon2.IDKey([]byte(password), salt, p.iterations, p.memory, p.parallelism, p.keyLength)

    // Check that the contents of the hashed passwords are identical. Note
    // that we are using the subtle.ConstantTimeCompare() function for this
    // to help prevent timing attacks.
    if subtle.ConstantTimeCompare(hash, otherHash) == 1 {
        return true, nil
    }
    return false, nil
}
 
func decodeHash(encodedHash string) (p *params, salt, hash []byte, err error) {
    vals := strings.Split(encodedHash, "$")
    if len(vals) != 6 {
        return nil, nil, nil, errors.New("the encoded hash is not in the correct format")
    }

    var version int
    _, err = fmt.Sscanf(vals[2], "v=%d", &version)
    if err != nil {
        return nil, nil, nil, err
    }
    if version != argon2.Version {
        return nil, nil, nil, errors.New("incompatible version of argon2")
    }

    p = &params{}
    _, err = fmt.Sscanf(vals[3], "m=%d,t=%d,p=%d", &p.memory, &p.iterations, &p.parallelism)
    if err != nil {
        return nil, nil, nil, err
    }

    salt, err = base64.RawStdEncoding.Strict().DecodeString(vals[4])
    if err != nil {
        return nil, nil, nil, err
    }
    p.saltLength = uint32(len(salt))

    hash, err = base64.RawStdEncoding.Strict().DecodeString(vals[5])
    if err != nil {
        return nil, nil, nil, err
    }
    p.keyLength = uint32(len(hash))

    return p, salt, hash, nil
} Python import argon2

argon2Hasher = argon2.PasswordHasher(
    time_cost=3, # number of iterations
    memory_cost=64 * 1024, # 64mb
    parallelism=1, # how many parallel threads to use
    hash_len=32, # the size of the derived key
    salt_len=16 # the size of the random generated salt in bytes
)


password = "somePassword"
 
hash = argon2Hasher.hash(password)
 
print("Hash + salt of password", hash)
 
verifyValid = argon2Hasher.verify(hash, password)
print("Password verification success:", verifyValid) Java import de.mkammerer.argon2.Argon2;
import de.mkammerer.argon2.Argon2Factory;
 
public class PasswordHashing {
    public static void main(String[] args) {
        // salt 32 bytes
        // Hash length 64 bytes
        Argon2 argon2 = Argon2Factory.create(
                Argon2Factory.Argon2Types.ARGON2id,
                16,
                32);

        char[] password = "somePassword".toCharArray();
        String hash = argon2.hash(3, // Number of iterations
                64 * 1024, // 64mb
                1, // how many parallel threads to use
                password);
        System.out.println("Hash + salt of the password: "+hash);
        System.out.println("Password verification success: "+ argon2.verify(hash, password));
    }
} Footnotes: Unless they are stored in a "secure vault" like this one. But then too, it’s still possible that they get leaked. Technically, the user’s password could be anything that, when hashed, results in . But if we try and log in with the password as , it would work since the algorithm is just matching the computed hash against the one in the database. "5994471abb01112afcc18159f6cc74b4f511b99806da59b3caf5a9c173cacfc5" "12345" https://cheatsheetseries.owasp.org/cheatsheets/Password_Storage_Cheat_Sheet.html Written by the Folks at — hope you enjoyed! We are always available on our server. Join us if you have any questions or need any help. SuperTokens Discord

Hashing, Salting, and Verifying Passwords in NodeJS, Python, Golang, and Java

It’s not just about Google and what coronavirus website Yes, the coronavirus pandemic is a threat for the tech industry. Consider that Apple, Facebook, Alphabet, Amazon, and Microsoft lost a combined $416.63 billion on March 12, Wall Street’s worst drop since the 1987 Black Market crash. Apple — the world’s most valuable tech company — and Microsoft lost more than $1 billion in value. Even a worse loss on the following Monday, March 16, when the Dow Jones index broke the grim record the markets set just a few days earlier. Yes, many on the tech companies rely on China — where the coronavirus has originated from and where the number of confirmed cases have skyrocketed to over 80,000 with more than 3,000 deaths — to build and assemble their hardware. Yes, — most tech companies have restricted or even halted travel of their employees and most are working from home. with the virus continuing to spread around the world with more than 180,000 cases in 120+ countries Now that US President Donald Trump, during his emergency declaration news conference in March 13, has pointed to Google and its parent company Alphabet as one of the companies helping the Administration in the fight against the virus, I think it’d be helpful to try understand the role of Big Tech — and technology in general — in the age of the coronavirus. “I want to thank Google,” . “Google is helping to develop a website. It’s going to be very quickly done, unlike websites of the past, to determine whether a test is warranted and to facilitate testing at a nearby convenient location.” President Trump said at a press conference in the Rose Garden at the White House The President pointed out: “Google has 1,700 engineers working on this right now. They’ve made tremendous progress. Our overriding goal is to stop the spread of the virus and to help all Americans who have been impacted by this.” However, according to Dieter Bohn of Verge Science, . it appears that Google is really not building such a website “Instead, a much smaller trial website made by another division of Alphabet, Google’s parent company, is going up,” writes Bohn. “It will only be able to direct people to testing facilities in the Bay Area.” Bohn explains: “Carolyn Wang, communications lead for Verily, told that the ‘triage website’ was initially only going to be made available to health care workers instead of the general public.” He continues: “Now that it has been announced the way it was, however, anybody will be able to visit it, she said. But the tool will be able to direct people to “pilot sites” for testing in the Bay Area, though Wang says Verily hopes to expand it beyond California ‘over time.’” The Verge only Brian Barrett and Louise Matsakis “A source at Google tells WIRED that company leadership was surprised that Trump announced anything about the initiative at the press conference. What he did say was also almost entirely wrong.” reports that 24 hours later, however, Google posted a new statement, tweeted by Alphabet and Google chief executive Sundar Pichai, with a different wording. “We’re partnering with the U.S. government in developing a website dedicated to COVID-19 education, prevention, and local resources nationwide,” the statement reads. “This includes best practices on prevention, links to authoritative information from the World Health Organization (WHO) and the Centers for Disease Control (CDC), and helpful tips and tools from Google for individuals, teachers and businesses. We’ll be rolling out an initial version of the website late Monday, March 16, and we’ll continue to enhance and update it with more resources on an ongoing basis.” At a White House press conference on March 15, President Trump said: “I want to thank the people at Google and Google Communications because, as you know, they substantiated what I said on Friday. The head of Google, who’s a great gentleman, said — called us and he apologized.” Here is the website that Google’s Verily launched on the evening of March 15, ahead of schedule: https://www.projectbaseline.com/study/covid-19/ It’s unclear whether, after the Rose Garden announcement, Google and the White House started to work together in a more coordinated way. What is clear however is that the role of technology and technology companies in the fight agains the coronavirus is multifaceted and pro-active on many levels. Obviously money is a big resource that Big Tech can provide and source. “Apple’s committed donations to the global COVID-19 response — both to help treat those who are sick and to help lessen the economic and community impacts of the pandemic — today reached $15 million worldwide,” , tweeted by chief executive Tim Cook. reads a statement by the company “We’re also announcing that we are matching our employee donations two-to-one to support COVID-19 response efforts locally, nationally and internationally.” “Facebook is matching up to $10 million in donations, and 100% of funds will directly support the work to prevent, detect and respond to the outbreak around the world,” . “We’ll also match $10 million for the CDC Foundation, which will launch a fundraiser in the next few weeks focused on combating the outbreak here in the US.” according to a post by Mark Zuckerberg, Facebook’s founder and chief executive In addition, Microsoft has partnered with the Seattle Foundation together with other philanthropy, government, and business partners “to create a that will rapidly deploy resources to community-based organizations at the frontlines of the coronavirus outbreak in the Puget Sound region” in Washington State. COVID-19 Response Fund The Fund has now raised almost $10 million. “I’m so inspired to see additional support for the COVID-19 Response Fund from employers, nonprofits and individuals throughout King County,” Brad Smith, President at Microsoft, . “The human impact from this outbreak continues to grow, and it’s important that we work together as a region to address it.” wrote on LinkedIn Also, Amazon has pledged to donate $5 million to small businesses around its Seattle headquarters that will be impacted by thousands of tech workers telecommuting in response to the COVID-19 outbreak. “These businesses support tens of thousands of local jobs that are a critical part of the Seattle and Puget Sound economy,” wrote Amazon’s global real estate chief, John Schoettler, in . “They’re our friends and neighbors, and we believe it’s important to try to help them confront the economic challenges that are likely to come from the COVID-19 outbreak.” a blog post Mark Benioff, founder and chief executive of Salesforce, announced on Twitter that the company “is donating $1 million to and $500K to the , which is meeting rapidly evolving response needs around the world.” UCSF’s COVID-19 Response Fund CDC Foundation’s Emergency Response Fund In a statement posted online, Salesforce said: “As always, we are matching our employees’ donations to all eligible organizations, and during this time are recommending they support organizations such as the CDC Foundation, UNICEF, Direct Relief, Save the Children, and Give2Asia.” In Italy, the country with the largest number of cases outside of China, crowdfunding platform GoFundMe has contributed €250,000 (around $275,000) launched by social media influencer Chiara Ferragni and her influencer and artist husband Fedez, which raised almost €4 million. The money will help a selected number of hospitals in Italy to increase ICU capacity. to a crowdfunding campaign If not money, the tech industry is also mobilizing in other ways, in addition to, in most cases, waiving canceling fees or other penalties due to unforeseen circumstances linked to the pandemic, like Airbnb and many other tech platforms in the travel and service sector. “In response to the extraordinary events and global disruption to travel caused by COVID-19, today we are announcing updated coverage under Airbnb’s Extenuating Circumstances policy,” . The new updated policy “allows hosts and guests to cancel eligible reservations with no charge or penalty.” a company statement reads But help comes in many forms. For example, Jack Ma, cofounder of the Chinese tech giant Alibaba, said in a statement posted on Twitter that his foundation will donate 500,000 COVID-19 testing kits and 1 million protective face masks to the US. “The crisis presents a huge challenge to all humankind in a globalized world,” Ma said. “The pandemic we face today can no longer be resolved by any individual country.” In other ways to help, Salesforce, for example, is “making our technology available to help our customers, partners, and communities.” They indicated that: -- Through , Salesforce will provide free access to technology for emergency response teams, call centers, and care management teams for health systems affected by coronavirus. Health Cloud -- Tableau has developed a to help organizations see and understand coronavirus data in near real-time. This includes case data compiled by Johns Hopkins University as well as data from the World Health Organization and the CDC. free data resource hub -- To help teams collaborate while employees are away from the office, to any Salesforce customer or non-profit organization through September 30, 2020. Quip Starter will be available for free Finally, misinformation is another major focus, in particular when it comes to social media platforms. Last week, in a meeting with tech representatives at the White House, the US administration “urged the tech industry to coordinate its efforts to stop the spread of coronavirus conspiracy theories on major social media sites, urging the companies to swap intelligence about harmful hoaxes before they go viral,” Tony Romm of the Washington Post reported, quoting three participants to the meeting. “Since the emergence and subsequent outbreak of COVID-19, there have been millions of Tweets and Retweets about the virus around the globe,” Alex Josephson and Eimear Lambe at Twitter Next, a global collective of strategists and planners, technologists and program managers, designers, engineers and data scientists — dedicated to finding the next way to get people talking about brands. write in a statement “We are seeing a COVID-19 related Tweet every 45 milliseconds and #Coronavirus is now the second most used hashtag of 2020. These volumes reflect the huge appetite for seeing and sharing news and information related to this virus as it unfolds.” In fact, as POLITICO reports, “In recent days, the world’s largest social media platforms have been pulling out all the stops to combat the wave of false reports, and outright lies that have spread like wildfire about COVID-19.” hacking attempts But it seems all efforts are not working that well and “people are sharing rumors, fake stories and half-truths about COVID-19 with each other directly across the likes of Instagram and Twitter as they struggle to understand how best to protect themselves and their families.” In this mayhem of misinformation and rush by social media platform to combat fake news about the virus, “Snap is different,” . Adweek’s Scott Nover reports Nover points out: “Snap, Ben Smith wrote, ‘largely avoided the misinormation trap’ and ‘is driving users toward good health advice.’ He’s right. In designing a social platform that largely avoids the quandary of misinformation, Snapchat can devote its resources to promoting reliable information.” “Snapchatters in the US are taking part in this timely and fast-moving global conversation” regarding the coronavirus health emergency and global pandemic. In a statement, Snap states that “At Snap, we are taking a proactive approach to ensuring our community has access to credible, fact-based information during these times,” the company says while highlighting a series of steps they have taken. These includes: the Discover platform curated in closely with only a select set of partners; offering accurate Coronavirus-related coverage through vetted partners, including the World Health Organization and the US Centers for Disease Control and Prevention (CDC), across seven countries and in six different languages. Here are some of the most-mentioned words and top trends in Snapchat Search, Public Snaps, and Our Story submissions in the US for March 13, 2020, according to Snap: In general, when it comes to messaging and point out users to the right information, platforms have been focusing on warnings and information hubs. Here are some examples: TikTok Twitter Instagram Google Search Nextdoor However, as POLITICO reports, “Tech companies and policymakers are finding that the tens of millions of euros and dollars they have spent to detect, monitor and combat sophisticated digital misinformation campaigns have little effect when regular social media users, and not foreign governments, are the ones spreading falsehoods.” “When you’re dealing with a pandemic, a lot of the stuff we’re seeing just crossed the threshold,” . “So it’s easier to set policies that are a little more black and white and take a much harder line.” Zuckerberg told Ben Smith in The New York Times But is it really easier? Analyst and misinformation experts are as positive as Zuckerberg. In the meantime, a group of social media platforms — Facebook, Google, LinkedIn, Microsoft, Reddit, Twitter, and YouTube — posted a joint statement on coronavirus misinformation. “We invite other companies to join us as we work to keep our communities healthy and safe,” the statement reads. Companies like Snap, TikTok, Pinterest, and Nextdoor are notably missing. TechCrunch’s Catherine Shu and Jonathan Shieber “an unprecedented move to reassure customers and flag the potential for misinformation about COVID-19 on their platforms.” They also point out: “tech companies aren’t just battling the spread of questionable posts by the public. They also have to contend with , including his tweets and Facebook posts.” call the statement misleading information in several of President Donald Trump’s public statements on COVID-19 Finally, some tech companies seem to benefit from this emergency. As millions of people around the world are confined at home. A look at the streaming business shows that most financial analysts who track Netflix, for example, “agree the coronavirus will keep people at home to stream more,” CNET. “Most of them grade Netflix’s business as either unlikely to be hurt by the pandemic, or possibly to be helped by it.” writes This is particularly true for new subscriptions. “The world is going to be streaming a hella lot of Netflix,” CNET continues. “You might think that means everyone would be flocking to sign up, and that Netflix’s business would be booming. But in the US, homebound viewers have more streaming options than ever with new entrants like Disney Plus and Apple TV Plus, not to mention Amazon Prime Video and Hulu. And chances are you already have a Netflix account.” As in everything, there’s another side of the coin, as the coronavirus has spurred streaming services, including Netflix and all the others to postpone productions.

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