What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy

By Apostolos Pyrgelis, Carmela Troncoso, Emiliano De Cristofaro. 2017

60 citations recorded by the source corpus.

Abstract Information about people’s movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users’ location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private. In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users’ locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary’s prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals’ punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data.

Subfields: Artificial Intelligence, Sociology and Political Science, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Sociology and Political Science

In Transportation

Citations in the corpus

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “Closer to our work is our own PETS’17 paper [22], which shows that aggregate location time-series can be used by an adversary to improve her prior knowledge about users’ location profiles.”
LOGAN: Membership Inference Attacks Against Generative Models (2017, neutral): “, profiling [49], property inference [4, 41], etc.”
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models (2019, background): “bility of a deep learning model. For models against which inference attacks are effective, owners can consider restricting their availability so as also to avoid other mounted attacks (e.g., proﬁling [65] or property inference [12,60]). The second motivation is to establish wrongdoing, where, e.g., ”
Toward Distribution Estimation under Local Differential Privacy with Small Samples (2018, background): “A recent study [38] also showed that the data utility can be completely destroyed in the case of time-series location data.”