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Section: New Results
Foundations of information hiding
Information hiding refers to the problem of protecting private information while performing certain tasks or interactions, and trying to avoid that an adversary can infer such information. This is one of the main areas of research in Comète; we are exploring several topics, described below.
On the information leakage of differentially-private mechanisms
Differential privacy aims at protecting the privacy of participants in statistical databases. Roughly, a mechanism satisfies differential privacy if the presence or value of a single individual in the database does not significantly change the likelihood of obtaining a certain answer to any statistical query posed by a data analyst. Differentially-private mechanisms are often oblivious: first the query is processed on the database to produce a true answer, and then this answer is adequately randomized before being reported to the data analyst. Ideally, a mechanism should minimize leakage, i.e., obfuscate as much as possible the link between reported answers and individuals' data, while maximizing utility, i.e., report answers as similar as possible to the true ones. These two goals, however, are in conflict with each other, thus imposing a trade-off between privacy and utility.
In [12] we used quantitative information flow principles to analyze leakage and utility in oblivious differentially-private mechanisms. We introduced a technique that exploits graph symmetries of the adjacency relation on databases to derive bounds on the min-entropy leakage of the mechanism. We considered a notion of utility based on identity gain functions, which is closely related to min-entropy leakage, and we derived bounds for it. Finally, given some graph symmetries, we provided a mechanism that maximizes utility while preserving the required level of differential privacy.
Geo-indistinguishability: A Principled Approach to Location Privacy
With the increasing popularity of handheld devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. In [17] we reported on our ongoing project aimed at protecting the privacy of the user when dealing with location-based services. The starting point of our approach is the principle of geo-indistinguishability, a formal notion of privacy that protects the user’s exact location, while allowing approximate information – typically needed to obtain a certain desired service – to be released. We then presented two mechanisms for achieving geo-indistinguishability, one generic to sanitize locations in any setting with reasonable utility, the other custom-built for a limited set of locations but providing optimal utility. Finally we extended our mechanisms to the case of location traces, where the user releases his location repeatedly along the day and we provide a method to limit the degradation of the privacy guarantees due to the correlation between the points. All the mechanisms were tested on real datasets and compared both among themselves and with respect to the state of the art in the field.
Constructing elastic distinguishability metrics for location privacy
The recently introduced notion of geo-indistinguishability tries to address the problem of accessing location-aware services in a privacy-friendly way by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map.
In [13] we proposed a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also showed how such an elastic metric can easily incorporate the concept of a "geographic fence" that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We performed an extensive evaluation of our technique by building an elastic metric for Paris' wide metropolitan area, using semantic information from the OpenStreetMap database. We compared the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results showed that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.
Quantitative Information Flow for Scheduler-Dependent Systems
Quantitative information flow analyses measure how much information on secrets is leaked by publicly observable outputs. One area of interest is to quantify and estimate the information leakage of composed systems. Prior work has focused on running disjoint component systems in parallel and reasoning about the leakage compositionally, but has not explored how the component systems are run in parallel or how the leakage of composed systems can be minimised.
In [23] we considered the manner in which parallel systems can be combined or scheduled. This considers the effects of scheduling channels where resources may be shared, or whether the outputs may be incrementally observed. We also generalised the attacker’s capability, of observing outputs of the system, to consider attackers who may be imperfect in their observations, e.g. when outputs may be confused with one another, or when assessing the time taken for an output to appear. Our main contribution was to present how scheduling and observation affect information leakage properties. In particular, that scheduling can hide some leaked information from perfect observers, while some scheduling may reveal secret information that is hidden to imperfect observers. In addition we presented an algorithm to construct a scheduler that minimises the min-entropy leakage and min-capacity in the presence of any observer.