Entries Tagged "de-anonymization"

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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

Tracking the Owner of Kickass Torrents

Here’s the story of how it was done. First, a fake ad on torrent listings linked the site to a Latvian bank account, an e-mail address, and a Facebook page.

Using basic website-tracking services, Der-Yeghiayan was able to uncover (via a reverse DNS search) the hosts of seven apparent KAT website domains: kickasstorrents.com, kat.cr, kickass.to, kat.ph, kastatic.com, thekat.tv and kickass.cr. This dug up two Chicago IP addresses, which were used as KAT name servers for more than four years. Agents were then able to legally gain a copy of the server’s access logs (explaining why it was federal authorities in Chicago that eventually charged Vaulin with his alleged crimes).

Using similar tools, Homeland Security investigators also performed something called a WHOIS lookup on a domain that redirected people to the main KAT site. A WHOIS search can provide the name, address, email and phone number of a website registrant. In the case of kickasstorrents.biz, that was Artem Vaulin from Kharkiv, Ukraine.

Der-Yeghiayan was able to link the email address found in the WHOIS lookup to an Apple email address that Vaulin purportedly used to operate KAT. It’s this Apple account that appears to tie all of pieces of Vaulin’s alleged involvement together.

On July 31st 2015, records provided by Apple show that the me.com account was used to purchase something on iTunes. The logs show that the same IP address was used on the same day to access the KAT Facebook page. After KAT began accepting Bitcoin donations in 2012, $72,767 was moved into a Coinbase account in Vaulin’s name. That Bitcoin wallet was registered with the same me.com email address.

Another article.

Posted on July 26, 2016 at 6:42 AMView Comments

Anonymization and the Law

Interesting paper: “Anonymization and Risk,” by Ira S. Rubinstein and Woodrow Hartzog:

Abstract: Perfect anonymization of data sets has failed. But the process of protecting data subjects in shared information remains integral to privacy practice and policy. While the deidentification debate has been vigorous and productive, there is no clear direction for policy. As a result, the law has been slow to adapt a holistic approach to protecting data subjects when data sets are released to others. Currently, the law is focused on whether an individual can be identified within a given set. We argue that the better locus of data release policy is on the process of minimizing the risk of reidentification and sensitive attribute disclosure. Process-based data release policy, which resembles the law of data security, will help us move past the limitations of focusing on whether data sets have been “anonymized.” It draws upon different tactics to protect the privacy of data subjects, including accurate deidentification rhetoric, contracts prohibiting reidentification and sensitive attribute disclosure, data enclaves, and query-based strategies to match required protections with the level of risk. By focusing on process, data release policy can better balance privacy and utility where nearly all data exchanges carry some risk.

Posted on July 11, 2016 at 6:31 AMView Comments

Tracking Anonymous Web Users

This research shows how to track e-commerce users better across multiple sessions, even when they do not provide unique identifiers such as user IDs or cookies.

Abstract: Targeting individual consumers has become a hallmark of direct and digital marketing, particularly as it has become easier to identify customers as they interact repeatedly with a company. However, across a wide variety of contexts and tracking technologies, companies find that customers can not be consistently identified which leads to a substantial fraction of anonymous visits in any CRM database. We develop a Bayesian imputation approach that allows us to probabilistically assign anonymous sessions to users, while ac- counting for a customer’s demographic information, frequency of interaction with the firm, and activities the customer engages in. Our approach simultaneously estimates a hierarchical model of customer behavior while probabilistically imputing which customers made the anonymous visits. We present both synthetic and real data studies that demonstrate our approach makes more accurate inference about individual customers’ preferences and responsiveness to marketing, relative to common approaches to anonymous visits: nearest- neighbor matching or ignoring the anonymous visits. We show how companies who use the proposed method will be better able to target individual customers, as well as infer how many of the anonymous visits are made by new customers.

Posted on February 5, 2016 at 6:56 AMView Comments

De-Anonymizing Users from their Coding Styles

Interesting blog post:

We are able to de-anonymize executable binaries of 20 programmers with 96% correct classification accuracy. In the de-anonymization process, the machine learning classifier trains on 8 executable binaries for each programmer to generate numeric representations of their coding styles. Such a high accuracy with this small amount of training data has not been reached in previous attempts. After scaling up the approach by increasing the dataset size, we de-anonymize 600 programmers with 52% accuracy. There has been no previous attempt to de-anonymize such a large binary dataset. The abovementioned executable binaries are compiled without any compiler optimizations, which are options to make binaries smaller and faster while transforming the source code more than plain compilation. As a result, compiler optimizations further normalize authorial style. For the first time in programmer de-anonymization, we show that we can still identify programmers of optimized executable binaries. While we can de-anonymize 100 programmers from unoptimized executable binaries with 78% accuracy, we can de-anonymize them from optimized executable binaries with 64% accuracy. We also show that stripping and removing symbol information from the executable binaries reduces the accuracy to 66%, which is a surprisingly small drop. This suggests that coding style survives complicated transformations.

Here’s the paper.

And here’s their previous paper, de-anonymizing programmers from their source code.

Posted on January 4, 2016 at 7:41 AMView Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.