There goes Wally: Anonymously sharing your location gives you away

Abstract

With current technology, a number of entities have access to user mobility traces at different levels of spatio-temporal granularity. At the same time, users frequently reveal their location through different means, including geo-tagged social media posts and mobile app usage. Such leaks are often bound to a pseudonym or a fake identity in an attempt to preserve one's privacy. In this work, we investigate how largescale mobility traces can de-anonymize anonymous location leaks. By mining the country-wide mobility traces of tens of millions of users, we aim to understand how many location leaks are required to uniquely match a trace, how spatiotemporal obfuscation decreases the matching quality, and how the location popularity and time of the leak influence de-anonymization. We also study the mobility characteristics of those individuals whose anonymous leaks are more prone to identification. Finally, by extending our matching methodology to full traces, we show how large-scale human mobility is highly unique. Our quantitative results have implications for the privacy of users' traces, and may serve as a guideline for future policies regarding the management and publication of mobility data.

You Are What You Write: Preserving Privacy in the Era of Large Language Models

“…Under this rubric, specific identifiers that can uniquely unmask an individual are removed-a national identification or social security number for instance-and replaced with synthetic identifiers created for this experiment. Unfortunately, a string of experimental results (Narayanan and Shmatikov 2009;Jawurek, Johns, and Rieck 2011;Biryukov, Khovratovich, and Pustogarov 2014;Pyrgelis et al 2019) as well as the case of the Netflix Prize Dataset (Narayanan and Shmatikov 2006), in which researchers were able to uniquely identify the majority of 'anonymous' participants through reference to other public sources of information, proved that maintaining personal privacy is harder than it might first appear; that in fact, our preferences, interactions, and the data we generate may be enough in themselves to reveal parts of our identity that we would prefer remain hidden.…”

A Large-scale Examination of ”Socioeconomic” Fairness in Mobile Networks

“…As mobile Internet access has become a vital resource for a large population, we believe it is imperative to examine potential unfairness or discrimination. While many works are made on large scale mobile network performance analysis (e.g.,, user mobility [12,30], network KPIs and planning [23,36], network performance metrics [9,41], and user Quality-of-Experience [3,31]), the body of literature does not employ the socioeconomic perspective. The literature on digital divide puts much emphasis on the topic.…”

Mobile Privacy: Scalable Ensemble Matching for User Identification Attacks

“…Two types of user identification can be roughly categorized, namely matching users from different data domains but in the same time [13], [27], [28] and matching the users from the same data domains in different time spans [10]. In addition, two types of location information, namely actual GPS coordinates [27], [31], [33] and base station location [6], [10], [32], are mainly studied in the literature.…”

“…+ q j j,l KL λ j,l λ ij,l . (32) where KL(λ 1 λ 2 ) denotes the KL divergence on two EXP distributions, i.e., KL(λ 1 λ 2 ) = log(λ 1 /λ 2 ) + (λ 2 /λ 1 ) − 1. Andλ ij,l is the weighted harmonic average overλ i,l andλ j,l .…”