Today marks the final day of broadcast for Pirate FM as Greatest Hits Radio takes over the airwaves tomorrow with a brand new local afternoon show.

Pirate FM final day as Greatest Hits Radio takes over | Falmouth Packet

The European Union’s counter-piracy naval operation off the Somali coast, known as Operation Atalanta, has reiterated its warning that ransom payments could potentially exacerbate the piracy issue off the Horn...

EU Naval Operation Warns Ransom Payments to Pirates Could Trigger More Attacks

The EU Atalanta naval force which handles piracy operations in the Indian Ocean has warned that the decision last week by a Bangladeshi company to pay ransom to free its ship and crew could lead to further attacks on merchant shipping. Ships sailing off the Somalian coasts were urged yesterday ...

EU naval force warns paying ransoms could lead to more Somali hijackings - Splash247

Iranian forces have seized the 15,000 teu MSC Aries as it was transiting the Straits of Hormuz, en route from Dubai to Asia

Iranian troops seize MSC box ship while Somali pirates net $5m ransom for bulker - The Loadstar

The Bangladesh-flagged cargo vessel and its crew were seized last month off Somalia&#x27;s coast.

Somali pirates free Bangladesh-flagged vessel, MV Abdullah

All 23 crew members on board the ship were released, according to a shipping firm CEO. Somalia has seen an uptick in piracy in recent months amid other threats to maritime commerce in the Red Sea and elsewhere.

Somali pirates free Bangladesh cargo ship after ransom paid

The Somali pirate crew that hijacked the Bangladeshi-flagged bulker Abdullah have received their ransom payout and have departed the ship, releasing...

Pirates Release Bulker Abdullah in Exchange for $5M Ransom

Release of the KSRM Group ship comes a month after it was taken 1,111 km northeast of Mogadishu

Pirates free hijacked bulk carrier after $5m ransom payment | TradeWinds

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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

Introduction This series is about: Knowing ourselves, our enemy & plan in a way to conjure "grounds" & "weather" to our advantage. This approach is adapted from a joint paper by and , published by (Switzerland 2016). I will share practical pointers through a series of questions related to ndustrial nternet- f- hings & perational echnology networks. Mohammed H. Almeshekah Eugene H. Spafford Springer International Publishing I I o T O T What is Cyber Deception? refers not just to typical Informations & Communications Technology, but also yber- hysical ystems that, when compromised, will incur safety & availability consequences. Deception is about faking it to achieve both early warning & deterrence but also diversions (from real assets) for the undeterred. But how is that achieved? Cyber C P S It always involves two basic steps, hiding the real (dissimulation) and showing the false (simulation). What are the Specific Considerations Related to Industrial Networks? Safety Risks Availability Risks Realism to attackers Secrecy The first 3 rimary onsiderations (or PCs in short) are self-explanatory. The 4th point depends on the overall objective. For instance, honeynets are meant to lure & collect intelligence; a lack of secrecy could ruin the entire effort. But for deterrence, secrecy may not be a PC since attackers may back off knowing that it is a trap. P C What are the Phases of a Cyber Deception Campaign? A campaign is divided into 3 phases: . We need to be mindful of the earlier considerations; Safety, Availability, Realism & depending on strategic goal(s), secrecy throughout the phases: Planning > Implementation & Integration > Monitoring & Evaluating A further break-down of the 3 Phases is as follow: The 1st two considerations of Safety & Availability are related to of identifying risks & countermeasures. I will explain the remaining steps along the way. An astute reader may ask: step 6 Why bother with all these, isn't there Deception 2.0 ommercial- ff- he- helf solutions? C O T S I will further explain how a combination of COTS together with custom deception to deal with Advanced Threat Actors by exploiting inherent mental biases that they may hold. Why combined? We assume Advanced Threat Actors to have the resources to figure out COTS Deception solutions & getting into our networks through routes we least expect. must In the next part of this series, I will cover . How to plan & measure success? Also Published Here

Taking a Systematic Approach to Cyber Deception - Part 1

The evolution of digital currency came about in part to address the issue of online payments. To decipher the question of truly private digital cash, we first need to understand the history of virtual currencies. Let’s take a look at past attempts of digital currencies. The idea of virtual payments dates back to the start of the dot com boom. One of the early notable attempts was by (1). Unfortunately, FirstVirtual was bogged down with a rather cumbersome payment process. First, the seller would send the sale details to FirstVirtual. Next, FirstVirtual bills the customer and collects their credit card information. In a seemingly archaic practice, FirstVirtual would hold the payment for up to 3 months. This is in case either party had any disputes. What is even more bizarre is that all communication was done turn by turn via email. FirstVirtual was one of the first noteworthy iterations of digital cash. David Chaum founded DigiCash in 1989 in Amsterdam. DigiCash was intended to counter the government's surveillance enabled by the electronic road toll systems. Two DigiCash products were released, eCash and Cyberbucks. Both currencies offered complete privacy between merchants and clients. However, DigiCash eventually fizzled out due to (a) collapse of the anonymity structure as merchants cashed out to banks, and (b) poor user experience due to the high friction in making transactions. DigiCash was the next somewhat successful player that came along in 1994. They used the Secure Electronic Transaction (SET) micropayments software architecture standardized by Visa, Mastercard and IBM. At its peak, users could pay charges as little as $0.10 and accounts were FDIC insured up to $100k. Unfortunately, the company was hit by the Y2K bug and eventually filed for bankruptcy. The intellectual property was sold and resold and eventually owned by Verisign, then Paypal today. CyberCash All of these still involve the legacy payment systems. What followed were several attempts at digital currency, each experimenting with Proof-of-Work algorithms, that were eventually fundamental precursors to Bitcoin. Adam Back’s Hashcash (1997), Wei Dai’s B-money (1998), Nick Szabo’s Bitgold (1998) and Reusable Proof-of-Work by Hal Finney (2004) (2). the world has to a currency with sound monetary policy. The virtues of Bitcoin are many but are beyond the premise of our discussion here. The public ledger of Bitcoin makes it at best pseudonymous. Although fiat is fraught with problems of poor monetary policy, physical cash beats Bitcoin in its privacy. No third party can track your spending when you use cash. The US dollar bill is still the most liquid and anonymous form of currency in the world today and is still the payment of choice in all matters political and criminal around the world (3-5). Bitcoin is the best solution The , however, is detrimental and even dangerous at many levels. Every transaction made reveals the balance of your wallet to the other party. It does not take any stretch of the imagination for large corporations to track you through your wallet transactions and vice versa. In fact, this is so much easier than trying to track you through your credit card transactions as those are sequestered in private servers. Whereas with Bitcoin, the blockchain is broadcast globally. transparency of Bitcoin Even worse, nefarious actors can target you once they have identified your generous wallet balance. This public display of your financial status to the world is found not just in Bitcoin but in also in every cryptocurrency without native privacy. Financial privacy is paramount to individuals and business entities. “Privacy is not about the right to be left alone but the right to exercise control. In our increasingly data driven economy, privacy is money.”  - Alex Gladstein, Human Rights Foundation. Truly private, digital cash is a fundamental pillar of any economy. There are numerous variants of Bitcoin that claim to be private, but almost all have optional privacy. One needs to know that optional privacy is NO privacy at all. It is worth noting that Wasabi’s optional privacy-for-a-fee service Samourai wallet is a frequently promoted privacy option for bitcoin. This wallet mixes up to 100 transactions using a method called Coinjoin. The caveat is that the fee increases according to the size of the anonymity set you choose. Choosing to hide your transaction in a group of 100 will also cost 100x more (6). Binance has already fired the first shot across bow freezing funds from a wallet that was sending bitcoin to a Samourai wallet address (7). The U.S. Justice Department Assistant Attorney General Brian Benczkowski declared the usage of Coinjoin mixers a crime (8). Due to the Coinjoin mixer that he operated, the CEO of DropBit was charged with money laundering in February 2020 (8). Players in the cryptocurrency privacy sector include top marketcap leaders such as DASH, ZCash, Monero, and forks of those such as PIVx (from DASH) and Sumokoin (from Monero). Let’s discuss the three marketcap leaders and household names of the privacy space that cryptofans would know about. First, DASH has the weakest privacy. It uses PrivateSend which is a method of mixing the transactions. This is a weak form of privacy. More egregious is that DASH offers privacy as an optional secondary feature. You see, DASH, like many cryptocurrencies focus on adoption and speed of transactions, with privacy as an afterthought. The state-of-the-art of privacy cryptography is “Zero-Knowledge Proof” (ZKP). ZKP validates the truth of something without revealing how that truth is known or sharing the content of this truth with the verifier. Hence, when applying ZKP to cryptocurrency, transactions can be validated without revealing any information about the sender’s address, the transaction amount or the receiver’s address. ZCash is a pioneer cryptocurrency that employed zero-knowledge proofs in their Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) algorithm. However, at the time of writing this article less than 10% of ZCash transactions are private (9). This creates a vulnerability for users that desire privacy, the other 90+% public transactions become datapoints, allowing for chain analytics to decipher the private transactions just like a sudoku puzzle. Arguably the most notorious, Monero (XMR) comes to mind when someone in crypto thinks of a privacy coin. Even John McAfee talks about Monero when asked about privacy: Monero uses Ring Confidential Transactions (RingCT) / Ring Signatures (RingSig) to confuse any inquisitor of transaction details. The algorithm essentially mixes the input from several transactions to obfuscate details. However, the algorithm only uses the data from a maximum of 15 transactions (upgraded from 7 recently). Monero has just implemented the Bulletproof algorithm to hide transaction values. What Monero does right is the enforcement of mandatory private transactions. So, despite dozens of projects that claim to be in the privacy sector today, Monero and Pirate Chain (ARRR) are the only two cryptocurrencies with non-optional (mandatory) private transactions where all transactions are private-only. possesses the best features of ZCash, the zk-SNARK algorithm, and Monero’s, default privacy enforcement. All addresses are by default private “shielded” z-addresses only, as are all transactions by default private z-transactions only. Pirate Chain This means that balances of every address are not visible to anyone else beside the user. There is no rich list. These transactions also cannot be tracked as there is no public display of the transaction on explorers. Another very unique feature of Pirate Chain is the anonymity set. It is purposefully designed to use every transaction that has ever occurred to create the anonymity set. With XMR your transactions are hidden only amongst a group of 15, but with ARRR, your transactions are forever obscured in an infinitely growing anonymity set. Even Monero’s developers at MoneroKon have praised Pirate for its anonymity set: Just as noteworthy is that both ZCash and Monero’s admins have publicly praised Pirate Chain for superior privacy. Pirate Chain is the first in the history of cryptocurrency to implement a mandatory private-only usage of zk-SNARKS. Privacy is not Pirate Chain’s only outstanding feature. Pirate Chain is also secured by Bitcoin’s network using delayed Proof of Work (dPOW). ARRR secures its blockchain by the use of 64 elected Notary Nodes (NNs). These NNs validate transactions then notarize them into both Komodo and Bitcoin’s blockchain. Attempts to overcome Pirate Chain with a Sybil attack would first have to overcome 51% of both Bitcoin and Komodo’s hashrates first. This is virtually impossible at the hashrate of both these chains today. As you can see, Pirate Chain is for all essential purposes the perfect privacy-first cryptocurrency by being absolutely and incomparably private, while at the same time undeniably secure in this sector. Even John McAfee favors Pirate Chain as his go to privacy option. https://twitter.com/officialmcafee/status/1213840656826818560?s=20 In today’s digital world, every action online is monitored and tracked. All financial transactions are at best recorded, or worse, hijacked by hackers for future illicit purchases. Privacy used to be the right to be left alone. Today it has morphed into the right to opt-out of giving away your personal info. Using Pirate Chain will restore privacy to your financial actions, giving you the right to opt-into sharing your personal details. Many other cryptocurrencies value adoption and coin price above privacy, with privacy features as a secondary feature. Pirate Chain’s ethos is privacy-first above all else. As the leading cryptocurrency with the best privacy, Pirate is the closest approximation to the US dollar in terms of anonymity and also boasts the sound fiscal policy of Bitcoin with programmed scarcity. While usage of $ARRR secures and anonymizes the financial transaction, there are still potential vectors of attack. The community at Pirate Chain came together to build a privacy-centric, operating system with the primary goal of protecting the user’s security and anonymity. When using a Pirate Chain wallet with PirateOS you are naturally more protected than any other wallet in crypto because it defends you with protections like VPN and Tor by default. The OS also boasts pro-active defenses like MAC address spoofing which gives you anonymity on your own local network and drive encryption to prevent unauthorized access. Welcome to the new Economy of Privacy, brought to you by the truly private digital cash and most anonymous cryptocurrency, Pirate Chain. : References 1. https://medium.com/@danielsfskim/the-early-history-of-digital-cash-and-cryptocurrency-b87436711de0 2. https://medium.com/@FidelityDigitalAssets/the-evolution-of-digital-cash-da19b06aa58e 3. https://www.zerohedge.com/news/2016-08-02/white-house-caught-secretly-airlifting-17-billion-us-taxpayer-cash-tehran-ensure-ira 4. https://www.cnn.com/2016/08/03/politics/us-sends-plane-iran-400-million-cash/index.html 5. https://www.theguardian.com/world/2007/feb/08/usa.iraq1 6. https://docs.wasabiwallet.io/FAQ/FAQ-UseWasabi.html#why-is-it-bad-to-re-use-addresses 7. https://news.bitcoin.com/as-fatf-regulations-galvanize-crypto-mixing-applications-are-targeted/ 8. https://www.justice.gov/opa/pr/ohio-resident-charged-operating-darknet-based-bitcoin-mixer-which-laundered-over-300-million 9.    Laura Shin. Unchained podcast episode 88, 10/16/18, 57’22’’

What Should You Expect From A Truly Private Digital Cash?

[Photo by Markus Spiske on Unsplash] A presentation on the fundamentals, roadmap, and capabilities needed for business transformation at the Nextea Innovation Gate Seminar in Milano, Italy. Operational excellence enables an enterprise and its leadership to continuously improve all areas of performance, including decision- making, ongoing investment, profitability, customer and partner services and human resources capabilities. Operationally excellent enterprises possess the processes and structures–or the “intangible assets”–that give them the visibility, control, tools, and management practices necessary to drive greater operational effectiveness and efficiency. Operational excellence had its genesis in manufacturing dating back to the pre-Industrial Revolution. In his 1776 magnum opus, , economist and philosopher Adam Smith was among the first great thinkers to define this now widely used concept. Smith famously described a small pin factory where 10 workers, each specializing in a different aspect of the job, could produce over 48,000 pins a day. Left to make a pin on his own, each of these workers might not have manufactured a single one in a day, and certainly not more than 20. The division of labor immensely increased the productivity of each worker. It’s still true today that assigning different roles and responsibilities across an enterprise enables scale, lowers costs and leads to greater operational efficiencies. The Wealth of Nations Delivering continuous improvement in the marketplace among competitors and customers requires enterprises to identify, understand and create the capabilities, behaviors and focuses necessary for repeatable, continuous and measurable operational improvement. Find out more from this presentation that I gave at a Event. Nextea Copyright © 2017 by Faisal Hoque. All rights reserved. . . . . . I am an . Founder of and other companies. Shadoka enables aspirations to lead, innovate, and transform. Shadoka’s accelerators and solutions bring together the management frameworks, digital platforms, and thought leadership to enable innovation, transformation, entrepreneurship, growth and social impact. entrepreneur and author SHADOKA Author of “ ” (McGraw Hill) and “ ” (Motivational Press). Follow me on Twitter . Use the leadership app and resiliancy app for free. Everything Connects — How to Transform and Lead in the Age of Creativity, Innovation and Sustainability Survive to Thrive: 27 Practices of Resilient Entrepreneurs, Innovators, and Leaders Faisal Hoque Everything Connects Suvvive to Thrive

Achieving Operational Excellence With AI And Machine Learning

Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal, it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment. Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. 70% of the toxic waste 70% of the toxic waste While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. computer purchases surged by 34% computer purchases surged by 34% The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. 100 times more gold 100 times more gold How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. bought a $40 hard drive bought a $40 hard drive What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. cost over $4.45 million cost over $4.45 million How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. roughly 800 laptops worth of e-waste roughly 800 laptops worth of e-waste Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. up to 40% of its e-waste up to 40% of its e-waste While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal , it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. international law making it illegal international law making it illegal What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment.

What Is E-Waste Hacking?

ই-ওয়েস্ট হ্যাকিং কি?

Was ist Elektroschrott-Hacking?

¿Qué es el pirateo de residuos electrónicos?

Qu’est-ce que le piratage des déchets électroniques ?

ई-कचरा हैकिंग क्या है?

電子廃棄物ハッキングとは何ですか?

전자 폐기물 해킹이란 무엇입니까?

O que é hackeamento de lixo eletrônico?

Что такое взлом электронных отходов?

E-Atık Hacking Nedir?

Hack rác thải điện tử là gì?

什么是电子垃圾黑客？

Not only did they completely revolutionize and redefine travel as we know it, but Airbnb was also one of the first-ever true “ ”, achieving a billion-dollar-plus evaluation. Unicorns They definitely haven’t slowed down over the years, surpassing over 1b in quarterly revenue, with an evaluation of over . They are nothing short of impressive. 35b It's not a surprise that startups are emulating and unpacking their business model to try to replicate their success. Let’s break down some of the main components that helped them achieve their massive success. Humble Beginnings Airbnb started out in 2008 when founders Brian Chesky and Joe Gebbia realized there was a business idea in renting our air mattresses to travelers in their living room when hotel space for a large conference in San Francisco was sold out. The idea then evolved into a website. Other people used the website to do the same thing. Boom. Airbnb. Do Things That Don’t Scale Instead of investing in paid ads, or social media, they started manually reaching out to people who would be the perfect client for their website/platform. Every time someone posted on Craigslist that they offered their home up for a bed and breakfast or for short-term lodging. They emailed them — asking them if they wanted to list their platform. This was manual, and this was tedious, but this worked. It was low-cost, and it worked. To quote Paul Graham, “Do things that don’t scale”. The Results? After initial success, they coded up an automated tool that reached out to new Craigslist postings automatically. They refined the copy, made the onboarding and sign-up simpler and more user-friendly. This led to viral growth and was the start of the incredible story of a category redefining company. Scaling & Growth Airbnb is a masterclass in startup strategy, marketing strategy, and sales strategy. They failed multiple times before they achieved any measure of success, and the early-stage lessons, that they used to achieve their initial momentum, as well as many scaling and growth strategies they used on their way to Unicorn status we’re equally impressive (and far too numerous to properly cover in a newsletter). When researching this case study, I stumbled across a 230+ page case study that goes into post-Craigslist, Airbnb growth strategies, and insights, so if you want to read more about some impressive, innovative things that Airbnb did to scale, check out these links. The Product The Website Paid Search Email Marketing SEO Social Media Content Marketing Referral Marketing Community Offline Lessons Learnt There are a few lessons we can learn from Airbnb’s Craigslist strategy. Growth doesn’t have to be sexy. Airbnb realized that startup growth could be using very traditional, boring, and simple methods to achieve initial growth. The Craigslist strategy was essentially an outbound sales strategy for a SaaS project. I know enough developers that just want to set up an inbound machine and never type an email or chat with a customer, and just drive traffic and optimize conversions. Do boring things that work. You don’t need paid ads, fancy funnels, graphic design. You need to write a few sentences and focus on getting customers wherever they’re at. Meet customers where they’re at. Airbnb didn’t focus on targeting, retargeting, and spreading a wide net. They just met their customers where they were at. I don’t think I’ve ever heard of a Silicon Valley thinking they wanted to spend their time on a platform as “unsexy” as Craigslist. But they realized that their customers were there — so they just spent time meeting the customers on the platform/medium they preferred. Be unconventional. Keep it simple, don’t overcomplicate a customer acquisition process. Just because your strategy isn’t in line with the strategy used by a CMO chatting about the latest high tech demand generation schemes on a popular marketing podcast, that doesn’t mean it’s wrong. Being unconventional is a good thing. Do things that don’t scale. To build out a process that will let you scale your business, it’s not going to be scalable on day 1. It won’t be automated; it won’t be available en masse. It will be slow and painful. A common trap that founders or marketers fall into is that they assume that if they build it, customers will come. This is the most dangerous mindset trap to fall into. Almost all successful businesses or strategies work because someone made it work. Figured out how to optimize it manually. This took a painstakingly long time to figure it out, and only then, once they proved that it was successful, figured out ways to scale it and automated it. Recruit users & land customers manually, and then scale that process, only once you’ve figured out that it works. Also published on . https://newsletter.roioverload.com/p/hacking-craigslist-for-viral-growth

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