Entries Tagged "de-anonymization"

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Someone Is Running Lots of Tor Relays

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.

Slashdot thread.

Posted on December 7, 2021 at 6:25 AMView Comments

Recovering Real Faces from Face-Generation ML System

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.

Posted on October 14, 2021 at 9:56 AMView Comments

De-anonymization Story

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.

Posted on July 28, 2021 at 6:03 AMView Comments

Commercial Location Data Used to Out Priest

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.

Posted on July 23, 2021 at 8:58 AMView Comments

Identifying the Person Behind Bitcoin Fog

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.

Based on tracing those financial transactions, the IRS says, it then identified Mt. Gox accounts that used Sterlingov’s home address and phone number, and even a Google account that included a Russian-language document on its Google Drive offering instructions for how to obscure Bitcoin payments. That document described exactly the steps Sterlingov allegedly took to buy the Liberty Reserve funds he’d used.

Posted on May 3, 2021 at 9:36 AMView Comments

Security Analysis of Apple’s “Find My…” Protocol

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.

There is also code available on GitHub, which allows arbitrary Bluetooth devices to be tracked via Apple’s Find My network.

Posted on March 15, 2021 at 6:16 AMView Comments

Tracking Users on Waze

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.

Posted on October 29, 2020 at 9:52 AMView Comments

Hackers Expose Russian FSB Cyberattack Projects

More nation-state activity in cyberspace, this time from Russia:

Per the different reports in Russian media, the files indicate that SyTech had worked since 2009 on a multitude of projects since 2009 for FSB unit 71330 and for fellow contractor Quantum. Projects include:

  • Nautilus — a project for collecting data about social media users (such as Facebook, MySpace, and LinkedIn).
  • Nautilus-S — a project for deanonymizing Tor traffic with the help of rogue Tor servers.
  • Reward — a project to covertly penetrate P2P networks, like the one used for torrents.
  • Mentor — a project to monitor and search email communications on the servers of Russian companies.
  • Hope — a project to investigate the topology of the Russian internet and how it connects to other countries’ network.
  • Tax-3 — a project for the creation of a closed intranet to store the information of highly-sensitive state figures, judges, and local administration officials, separate from the rest of the state’s IT networks.

BBC Russia, who received the full trove of documents, claims there were other older projects for researching other network protocols such as Jabber (instant messaging), ED2K (eDonkey), and OpenFT (enterprise file transfer).

Other files posted on the Digital Revolution Twitter account claimed that the FSB was also tracking students and pensioners.

Posted on July 22, 2019 at 6:17 AMView Comments

Identifying Programmers by Their Coding Style

Fascinating research on de-anonymizing code — from either source code or compiled code:

Rachel Greenstadt, an associate professor of computer science at Drexel University, and Aylin Caliskan, Greenstadt’s former PhD student and now an assistant professor at George Washington University, have found that code, like other forms of stylistic expression, are not anonymous. At the DefCon hacking conference Friday, the pair will present a number of studies they’ve conducted using machine learning techniques to de-anonymize the authors of code samples. Their work could be useful in a plagiarism dispute, for instance, but it also has privacy implications, especially for the thousands of developers who contribute open source code to the world.

Posted on August 13, 2018 at 4:02 PMView Comments

De-Anonymizing Browser History Using Social-Network Data

Interesting research: “De-anonymizing Web Browsing Data with Social Networks“:

Abstract: Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show — theoretically, via simulation, and through experiments on real user data — that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world effectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is — to our knowledge — the largest scale demonstrated de-anonymization to date.

Posted on February 10, 2017 at 8:25 AMView Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.