Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/01/the-fbi-identified-a-tor-user.html
No details, though:
According to the complaint against him, Al-Azhari allegedly visited a dark web site that hosts “unofficial propaganda and photographs related to ISIS” multiple times on May 14, 2019. In virtue of being a dark web site—that is, one hosted on the Tor anonymity network—it should have been difficult for the site owner’s or a third party to determine the real IP address of any of the site’s visitors.
Yet, that’s exactly what the FBI did. It found Al-Azhari allegedly visited the site from an IP address associated with Al-Azhari’s grandmother’s house in Riverside, California. The FBI also found what specific pages Al-Azhari visited, including a section on donating Bitcoin; another focused on military operations conducted by ISIS fighters in Iraq, Syria, and Nigeria; and another page that provided links to material from ISIS’s media arm. Without the FBI deploying some form of surveillance technique, or Al-Azhari using another method to visit the site which exposed their IP address, this should not have been possible.
There are lots of ways to de-anonymize Tor users. Someone at the NSA gave a presentation on this ten years ago. (I wrote about it for the Guardian in 2013, an essay that reads so dated in light of what we’ve learned since then.) It’s unlikely that the FBI uses the same sorts of broad surveillance techniques that the NSA does, but it’s certainly possible that the NSA did the surveillance and passed the information to the FBI.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/07/new-browser-de-anonymization-technique.html
Researchers have a new way to de-anonymize browser users, by correlating their behavior on one account with their behavior on another:
The findings, which NJIT researchers will present at the Usenix Security Symposium in Boston next month, show how an attacker who tricks someone into loading a malicious website can determine whether that visitor controls a particular public identifier, like an email address or social media account, thus linking the visitor to a piece of potentially personal data.
When you visit a website, the page can capture your IP address, but this doesn’t necessarily give the site owner enough information to individually identify you. Instead, the hack analyzes subtle features of a potential target’s browser activity to determine whether they are logged into an account for an array of services, from YouTube and Dropbox to Twitter, Facebook, TikTok, and more. Plus the attacks work against every major browser, including the anonymity-focused Tor Browser.
“Let’s say you have a forum for underground extremists or activists, and a law enforcement agency has covertly taken control of it,” Curtmola says. “They want to identify the users of this forum but can’t do this directly because the users use pseudonyms. But let’s say that the agency was able to also gather a list of Facebook accounts who are suspected to be users of this forum. They would now be able to correlate whoever visits the forum with a specific Facebook identity.”
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/04/de-anonymizing-bitcoin.html
Andy Greenberg wrote a long article — an excerpt from his new book — on how law enforcement de-anonymized bitcoin transactions to take down a global child porn ring.
Within a few years of Bitcoin’s arrival, academic security researchers — and then companies like Chainalysis — began to tear gaping holes in the masks separating Bitcoin users’ addresses and their real-world identities. They could follow bitcoins on the blockchain as they moved from address to address until they reached one that could be tied to a known identity. In some cases, an investigator could learn someone’s Bitcoin addresses by transacting with them, the way an undercover narcotics agent might conduct a buy-and-bust. In other cases, they could trace a target’s coins to an account at a cryptocurrency exchange where financial regulations required users to prove their identity. A quick subpoena to the exchange from one of Chainalysis’ customers in law enforcement was then enough to strip away any illusion of Bitcoin’s anonymity.
Chainalysis had combined these techniques for de-anonymizing Bitcoin users with methods that allowed it to “cluster” addresses, showing that anywhere from dozens to millions of addresses sometimes belonged to a single person or organization. When coins from two or more addresses were spent in a single transaction, for instance, it revealed that whoever created that “multi-input” transaction must have control of both spender addresses, allowing Chainalysis to lump them into a single identity. In other cases, Chainalysis and its users could follow a “peel chain” — a process analogous to tracking a single wad of cash as a user repeatedly pulled it out, peeled off a few bills, and put it back in a different pocket. In those peel chains, bitcoins would be moved out of one address as a fraction was paid to a recipient and then the remainder returned to the spender at a “change” address. Distinguishing those change addresses could allow an investigator to follow a sum of money as it hopped from one address to the next, charting its path through the noise of Bitcoin’s blockchain.
Thanks to tricks like these, Bitcoin had turned out to be practically the opposite of untraceable: a kind of honeypot for crypto criminals that had, for years, dutifully and unerasably recorded evidence of their dirty deals. By 2017, agencies like the FBI, the Drug Enforcement Agency, and the IRS’s Criminal Investigation division (or IRS-CI) had traced Bitcoin transactions to carry out one investigative coup after another, very often with the help of Chainalysis.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/someone-is-running-lots-of-tor-relays.html
Since 2017, someone is running about a thousand — 10% of the total — Tor servers in an attempt to deanonymize the network:
Grouping these servers under the KAX17 umbrella, Nusenu says this threat actor has constantly added servers with no contact details to the Tor network in industrial quantities, operating servers in the realm of hundreds at any given point.
The actor’s servers are typically located in data centers spread all over the world and are typically configured as entry and middle points primarily, although KAX17 also operates a small number of exit points.
Nusenu said this is strange as most threat actors operating malicious Tor relays tend to focus on running exit points, which allows them to modify the user’s traffic. For example, a threat actor that Nusenu has been tracking as BTCMITM20 ran thousands of malicious Tor exit nodes in order to replace Bitcoin wallet addresses inside web traffic and hijack user payments.
KAX17’s focus on Tor entry and middle relays led Nusenu to believe that the group, which he described as “non-amateur level and persistent,” is trying to collect information on users connecting to the Tor network and attempting to map their routes inside it.
In research published this week and shared with The Record, Nusenu said that at one point, there was a 16% chance that a Tor user would connect to the Tor network through one of KAX17’s servers, a 35% chance they would pass through one of its middle relays, and up to 5% chance to exit through one.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/10/recovering-real-faces-from-face-generation-ml-system.html
New paper: “This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces.
Abstract: Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the other hand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern.
News article. Slashdot post.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/07/de-anonymization-story.html
This is important:
Monsignor Jeffrey Burrill was general secretary of the US Conference of Catholic Bishops (USCCB), effectively the highest-ranking priest in the US who is not a bishop, before records of Grindr usage obtained from data brokers was correlated with his apartment, place of work, vacation home, family members’ addresses, and more.
The data that resulted in Burrill’s ouster was reportedly obtained through legal means. Mobile carriers sold — and still sell — location data to brokers who aggregate it and sell it to a range of buyers, including advertisers, law enforcement, roadside services, and even bounty hunters. Carriers were caught in 2018 selling real-time location data to brokers, drawing the ire of Congress. But after carriers issued public mea culpas and promises to reform the practice, investigations have revealed that phone location data is still popping up in places it shouldn’t. This year, T-Mobile even broadened its offerings, selling customers’ web and app usage data to third parties unless people opt out.
The publication that revealed Burrill’s private app usage, The Pillar, a newsletter covering the Catholic Church, did not say exactly where or how it obtained Burrill’s data. But it did say how it de-anonymized aggregated data to correlate Grindr app usage with a device that appears to be Burrill’s phone.
The Pillar says it obtained 24 months’ worth of “commercially available records of app signal data” covering portions of 2018, 2019, and 2020, which included records of Grindr usage and locations where the app was used. The publication zeroed in on addresses where Burrill was known to frequent and singled out a device identifier that appeared at those locations. Key locations included Burrill’s office at the USCCB, his USCCB-owned residence, and USCCB meetings and events in other cities where he was in attendance. The analysis also looked at other locations farther afield, including his family lake house, his family members’ residences, and an apartment in his Wisconsin hometown where he reportedly has lived.
Location data is not anonymous. It cannot be made anonymous. I hope stories like these will teach people that.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/07/commercial-location-data-used-to-out-priest.html
A Catholic priest was outed through commercially available surveillance data. Vice has a good analysis:
The news starkly demonstrates not only the inherent power of location data, but how the chance to wield that power has trickled down from corporations and intelligence agencies to essentially any sort of disgruntled, unscrupulous, or dangerous individual. A growing market of data brokers that collect and sell data from countless apps has made it so that anyone with a bit of cash and effort can figure out which phone in a so-called anonymized dataset belongs to a target, and abuse that information.
There is a whole industry devoted to re-identifying anonymized data. This was something that Snowden showed that the NSA could do. Now it’s available to everyone.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/10/tracking-users-on-waze.html
A security researcher discovered a wulnerability in Waze that breaks the anonymity of users:
I found out that I can visit Waze from any web browser at waze.com/livemap so I decided to check how are those driver icons implemented. What I found is that I can ask Waze API for data on a location by sending my latitude and longitude coordinates. Except the essential traffic information, Waze also sends me coordinates of other drivers who are nearby. What caught my eyes was that identification numbers (ID) associated with the icons were not changing over time. I decided to track one driver and after some time she really appeared in a different place on the same road.
The vulnerability has been fixed. More interesting is that the researcher was able to de-anonymize some of the Waze users, proving yet again that anonymity is hard when we’re all so different.