Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted ty...

Location privacy is a major concern in an increasingly connected and highly pervasive network of mobile users. Novel location-based applications and device-to-device services (on these mobile devices) are gaining popularity, but at the same time, these services allow curious service providers and eavesdroppers to track users and their movements. Earlier research efforts on location-privacy pres...

Privacy concerns often prevent organizations from sharing data for data mining purposes. There has been a rich literature on privacy preserving data mining techniques that can protect privacy and still allow accurate mining. Many such techniques have some parameters that need to be set correctly to achieve the desired balance between privacy protection and quality of mining results. However, th...

The approximation introduced by finite-precision representation of continuous data can induce arbitrarily large information leaks even when the computation using exact semantics is secure. Such leakage can thus undermine design efforts aimed at protecting sensitive information. We focus here on differential privacy, an approach to privacy that emerged from the area of statistical databases and ...

Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and to perform alignment, which greatly limits their applications when are restricted due privacy concerns. In this paper we address challenging hypothesis transfer setting for UODA, from no longer available during domain...

radio frequency identification (rfid) applications have spread all over the world. in order to provide their security and privacy, researchers proposed different kinds of protocols. in this paper, we analyze the privacy of a new protocol, proposed by yu-jehn in 2015 which is based on electronic product code class1 generation 2 (epc c1 g2) standard. by applying the ouafi_phan privacy model, we s...

In this paper, we consider the adaptation of differential privacy to the context of locationbased services (LBSs), which personalize the information provided to a user based on his current position. Assuming that the LBS provider is queried with a perturbed version of the position of the user instead of his exact one, we rely on differential privacy to quantify the level of indistinguishability...

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