Quantifying Interdependent Privacy Risks with Location Data

Abstract

Abstract-Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users' IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users' locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

“…In the case where co-located users are directly tagged by the uploader, detection is, obviously, no longer needed. The context provided to a consenter in the case of co-location data could also include an estimation of the location privacy loss stemming from that reported co-location, as proposed by Olteanu et al [53]. However, as co-locations introduce dependencies among the data of different users, once a co-location between Alice and Bob is shared, Alice's future location posts would also affect Bob's location privacy and vice versa.…”

“…Beyond MSPD data, recent studies showed that seemingly strictly personal data reveals information about other individuals [18], [36], [45], [53]. This concept is referred to as interdependent personal data (IPD; the term interdependent privacy was coined by Biczók and Chia [12]).…”

On the Privacy Implications of Location Semantics

“…The main differences between our work and Shokri et al's are (1) the use of general Bayesian networks to model users' behavior and (2) a two-step background construction (i.e., first semantic, then geographical) to deal with sparse data. Similarly, but orthogonal, to our work, in [12], the authors study the effect of co-location information (e.g., Alice and Bob are at the same (unknown) location at 2pm) on users' location privacy. As for obfuscation mechanisms, a detailed survey can be found in [1].…”

“…This however, makes existing techniques for background construction inefficient due to the sparsity of the transition data (although many transitions go from one geographical region to another, the number of transitions from a couple (region, semantic tag) to another is significantly reduced). Also, recent work have shown that moving from Hidden Markov Models to Bayesian networks enables the adversary to take into account more complex information such as colocation [12]. The main differences between our work and Shokri et al's are (1) the use of general Bayesian networks to model users' behavior and (2) a two-step background construction (i.e., first semantic, then geographical) to deal with sparse data.…”

Covert channels in modern computer systems : the cases of mobile and cloud

“…Location tracking has achieved significant attention in the research community [44,96,73,67,92] and great notoriety in the news cycle. In late 2017, Quartz [16] reported that Android devices sent location data to Google when they were within range of a new cell tower.…”