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🕵️ Crypto Mixers and Privacy Coins: Can They Resist Censorship? 🕵️

US sanctions on Tornado Cash smart contracts have created new regulatory challenges for crypto mixers and privacy coins

In response to the US Treasury sanctioning crypto mixer Tornado Cash, advocacy groups such as Coin Center have come to its defense — arguing that smart contract code is not a sanctionable entity.

With this new precedent, it is unclear if privacy coins such as Monero will face similar censorship. A hard fork update on Aug. 13 reportedly made Monero transactions harder to trace — potentially closing any back doors law agencies used to track transactions.

The view that any cryptocurrency transaction is private by default is a common misconception. In fact, the opposite is true. Blockchain data is public and transactions are traceable. Crypto mixers and privacy coins were created to provide privacy for this open financial system. But both face different uphill battles. Before analyzing the likelihood of either’s success, we need to explain how they work, where they differ and the regulatory strategy game of financial censorship.

So what is a crypto mixer?
A crypto mixer, also known as a tumbler or blender, is a transaction mixing tool or service that anyone can use to obscure a crypto wallet’s source of funds. These tools were first created for bitcoin in 2013 but became a popular alternative to privacy coins once solutions like Tornado Cash made it available for a variety of cryptoassets.

There are two types of crypto mixers: custodial and non-custodial. Custodial blenders such as blender.io are central entities that take full custody of funds to mix transactions. Users pay a fee for the service and trust the entity to return their funds once the transactions are blended.

Blender.io was the first mixer to be sanctioned by US Department of the Treasury’s Office of Foreign Assets Control (OFAC). It did not receive the same attention as Tornado Cash because it fell under the pattern of previous sanctions made against persons and entities. A North Korean state-sponsored hacker collective known as the Lazarus Group reportedly used the service after a hack against Axie Infinity that resulted in a $620 million loss.

How non-custodial crypto mixers like Tornado Cash works
With Tornado Cash, users send funds to smart contract addresses that automatically mix deposits of the same amount. They then use a zero-knowledge proof contract to prove they have the right to withdraw that amount.

For example, say you want to mix 11 ETH. Tornado Cash’s smart contracts group deposits by amounts. So you could deposit 10 ETH to the 10 ETH mixer and 1 ETH to the 1 ETH mixer. Once funds are sent to each blender, the contracts then use zero-knowledge proofs to verify you sent a deposit to each one without knowing which one was originally yours. This essentially gives you the equivalent of a withdrawal permission slip for each mixer.

So if you were to use the permission slips to withdraw both deposits, it would be close to impossible for any outside observer to identify the correct source of funds. They would see a myriad of potential options.

The tool provides pretty good financial privacy by breaking the link between the sender and receiver. But it’s not perfect; theoretically, third party blockchain intelligence could use outside data and behavior models in an attempt to deduce which transaction history belongs to the tokens on your new wallet address.

Legal challenges
On Aug. 8, 2022, OFAC added a list of addresses associated with Tornado Cash to the same list of sanctioned addresses where Blender.io ended up. This was in response to news that the Lazarus Group used the tool to launder $455 million in stolen funds.

OFAC used the same messaging and reasoning as it did Blender.io, but it did not acknowledge the key custodial difference between the two. In Coin Center’s full analysis, they argue that Tornado Cash has two separate elements: The decentralized group of governing members they call “Tornado Cash Entity” and the immutable smart contract coin mixers they call “Tornado Cash Application.”

The Tornado Cash Entity cannot update or change the Tornado Cash Application because the original creators destroyed their admin keys. The smart contracts will exist as long as the Ethereum blockchain continues to operate. So even though the Tornado Cash website is down, anyone can spin up a new front end — or interface with the smart contracts directly — that lets users access the same mixers.

The problem is that OFAC included these immutable smart contract addresses in the list of sanctions. So there are now innocent Americans with funds still in these mixers. If they attempt to move the funds, they will be breaking the law and subject to penalty. And because the application is not an entity, it has no means to petition OFAC for sanction removal.

Coin Center further argues that because the Tornado Cash Application is not an entity, OFAC did not cite the proper authority to add the smart contract addresses to the sanctions list. This marks an unprecedented move with potential constitutional issues.

In response to OFAC’s announcement, companies agreed to censor anyone connected to these addresses. The decentralized finance app Aave blocked any users that had Tornado Cash funds sent to them in a dust attack. And Circle followed by freezing 75,000 usd coin stablecoins belonging to Tornado Cash users. The Blockworks’ Empire podcast explains how that is possible in a Twitter thread.

What are privacy coins and how do they differ?
Privacy coins are cryptocurrencies that use a variety of approaches to obscure IP addresses, wallet balances and the flow of funds from public view. They differ from crypto mixers in that they make financial privacy less of a feature and more of a product. As a result, they only provide privacy to transactions made in a specific currency.

The two most popular privacy coins are Z-cash and Monero. Z-cash is a cryptocurrency that relies primarily on zero-knowledge proofs to shield transaction info. In October 2018, Z-cash announced that they fixed an 8-month-old bug in proofs that could have permitted an infinite inflation of supply. Due to transaction privacy, it was unclear how much was actually inflated.

Since this early stumble, z-cash has never returned to the highs of the 2017 bull cycle and currently ranks second to Monero in total privacy coin market cap. While monero was able to once again reach similar prices of the 2017 market, it failed to break its all-time high in 2021.

Monero is a privacy coin that offers financial anonymity through layers of privacy-enhanced blockchain encryption. Every transaction utilizes single-use stealth addresses to prevent the visibility of public address balances. So only users with a wallet’s private key can map its balance back to a public address. It also uses ring signatures to obscure the source of funds in a transaction by including random addresses in the verification signature.

Privacy challenges
The Monero protocol was upgraded on Aug. 13. While the previous version of Monero offered a layer of privacy, its complete untraceability was debatable. In 2018, critics claimed that inputs in a signature ring could be deduced through a process of elimination. And in 2021, CipherTracer reportedly patented a method that the Department of Homeland Security (DHS) uses to trace transactions.

Even if CipherTracer discovered real vulnerabilities, the extent of their impact is unclear. They didn’t disclose their methods or success rate. This previous version still provided a degree of financial privacy in the sense that it blocked anyone not willing to pay CipherTracer.

But this disincentive is less resistant to state sanctions and censorship. Theoretically, the state is more willing to spend resources in an attempt to trace addresses — especially if they suspect a connection to crime, or in some countries, political opposition.

In Canada, an effort was made to trace financial contributions to the trucker freedom convoy. The government ended up sanctioning 34 crypto wallets in connection to the movement, and Monero addresses were included in that list.

The Monero developers hope this update will close any potential vulnerability by increasing the number of transactions in a ring signature. But in response to the update, CipherTracer stated, “While Monero’s upcoming chain improvements are significant, the fundamentals of our approach to tracing probable source of funds will still apply after the fork.”

If the upgrade does succeed in closing these back doors, there is concern that OFAC may take similar actions against Monero. In an interview with CoinDesk, a Monero contributor said that, “at the moment, I’m not concerned about immediate legal action.”

“There is no direct financial incentive…for developers, unlike [the situation with] the Tornado Cash developer,” he said.

These comments seem to infer that the potential ability for the developer to profit from the use of these smart contracts makes him liable. Dutch financial crimes agency FIOD arrested a Tornado Cash developer on suspicion of laundering money through the tool. But it is unclear if that arrest was for his specific attempts to launder money or for his connection to others using it for that purpose.

Adoption challenges
Even though top privacy coins such as monero and z-cash are actively working to increase the privacy of transactions, they have not seen the same degree of adoption as leading layer-1 blockchains such as Ethereum. Many competitors, including Secret Network and Oasis Network, argue that the reason for this lag is that privacy coins do not offer a base layer of privacy that can be used to build Web3.

In 2020 Secret Network was the first privacy based blockchain to enable smart contract programmability. It lives in the Cosmos ecosystem and is working toward a vision of Web3 privacy. It has launched multiple apps such as the decentralized messaging service Altermail, and decentralized exchange SiennaSwap.

But Secret Network and its competitors face the classic challenge of an overcrowded sector. They still have a long way in overcoming the market dominance of Monero and Z-Cash. The threat of sanctions have motivated many in the Z-Cash community to explore creating their own smart contract programmability.

The future of digital financial privacy
The battle against financial privacy feels like a game of whack-a-mole. So far, the state has tried two different tools. With crypto mixers, they used the regulatory sanctions hammer. And for privacy coins, they tried blockchain intelligence sleuths.

Their approach may be, if one financial privacy method is too popular with criminals or too hard to trace, they will just shut it down with the hammer.

Advocacy groups such as Coin Center may respond by challenging such actions in court, but that process will take years. The sanctions are very likely hurting innocent Americans in the meantime.

For other privacy solutions, they may use investigations to continue in their cat and mouse chase with developer upgrades.

User adoption, though, is a key element to this game. As more people are drawn to either mixers or privacy coins, the chance of tracing transactions becomes exponentially difficult. Switching analogies, it’s like the classic police chase down a narrow alley. If the suspect reaches a bustling parade, they can dust off and subtly slip away into the crowd.

If a privacy coin, mixer or base-layer privacy solution gains mainstream adoption, it could have greater resistance to censorship. State officials would struggle to find the political backing for sweeping sanctions or technology needed to crack privacy measures. And the potential Tornado Cash sanctions fallout for Ethereum validators may pull millions more into this conversation.

https://blockworks.co/crypto-mixers-and-privacy-coins-can-they-resist-censorship/

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.

It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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