Collaborate Disseminate
The German police have successfully deanonymized at least four Tor users. It appears they watch known Tor relays and known suspects, and use timing analysis to figure out who is using what relay.
Tor has written about this.
Hacker News thread.
Continue reading Law Enforcement Deanonymizes Tor Users
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…
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…
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…
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…
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…
Continue reading Recovering Real Faces from Face-Generation ML System
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 co… Continue reading De-anonymization Story
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…
Continue reading Commercial Location Data Used to Out Priest
The person behind the Bitcoin Fog was identified and arrested. Bitcoin Fog was an anonymization service: for a fee, it mixed a bunch of people’s bitcoins up so that it was hard to figure out where any individual coins came from. It ran for ten years.
Identifying the person behind Bitcoin Fog serves as an illustrative example of how hard it is to be anonymous online in the face of a competent police investigation:
Most remarkable, however, is the IRS’s account of tracking down Sterlingov using the very same sort of blockchain analysis that his own service was meant to defeat. The complaint outlines how Sterlingov allegedly paid for the server hosting of Bitcoin Fog at one point in 2011 using the now-defunct digital currency Liberty Reserve. It goes on to show the blockchain evidence that identifies Sterlingov’s purchase of that Liberty Reserve currency with bitcoins: He first exchanged euros for the bitcoins on the early cryptocurrency exchange Mt. Gox, then moved those bitcoins through several subsequent addresses, and finally traded them on another currency exchange for the Liberty Reserve funds he’d use to set up Bitcoin Fog’s domain…
Interesting research: “Who Can Find My Devices? Security and Privacy of Apple’s Crowd-Sourced Bluetooth Location Tracking System“:
Abstract: Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world’s largest crowd-sourced location tracking network called offline finding (OF). OF leverages online finder devices to detect the presence of missing offline devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, untrackability of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user’s top locations with an error in the order of 10 meters in urban areas. While we find that OF’s design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available…
Continue reading Security Analysis of Apple’s “Find My…” Protocol