Finding close T-indistinguishability operators to a given proximity

Two ways to approximate a proximity relation R (i.e. a reflexive and symmetric fuzzy relation) by a T-transitive one where T is a continuous archimedean t-norm are given. The first one aggregates the transitive closure R of R with a (maximal) T-transitive relation B contained in R. The second one modifies the values of R or B to better fit them with the ones of R.Peer ReviewedPostprint (published version

Approximating proximities by similarities

In this paper an algorithm to find a similarity close to a proximity or tolerance relation R is given. The obtained similarity is closer to R than its transitive closure or any transitive opening of R.Peer ReviewedPostprint (published version

An algorithm to compute the transitive closure, a transitive approximation and a transitive opening of a proximity

A method to get the transitive closure, a transitive opening and a transitive approximation of a reflexive and symmetric fuzzy relation is presented. The method builds at the same time a binary partition tree for the output similarities.Peer ReviewedPreprin

Fifty years of similarity relations: a survey of foundations and applications

On the occasion of the 50th anniversary of the publication of Zadeh's significant paper Similarity Relations and Fuzzy Orderings, an account of the development of similarity relations during this time will be given. Moreover, the main topics related to these fuzzy relations will be reviewed.Peer ReviewedPostprint (author's final draft

A uniformity-based approach to location privacy

As location-based services emerge, many people feel exposed to high privacy threats. Privacy protection is a major challenge for such services and related applications. A simple approach is perturbation, which adds an artificial noise to positions and returns an obfuscated measurement to the requester. Our main finding is that, unless the noise is chosen properly, these methods do not withstand attacks based on statistical analysis. In this paper, we propose UniLO, an obfuscation operator which offers high assurances on obfuscation uniformity, even in case of imprecise location measurement. We also deal with service differentiation by proposing three UniLO-based obfuscation algorithms that offer multiple contemporaneous levels of privacy. Finally, we experimentally prove the superiority of the proposed algorithms compared to the state-of-the-art solutions, both in terms of utility and resistance against inference attacks

Transitive Openings

Peer ReviewedPostprint (published version

Time Distortion Anonymization for the Publication of Mobility Data with High Utility

An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated anonymization techniques, often based on adding noise to the spatial data. However, these techniques either compromise the privacy if the added noise is too little or the utility of the data if the added noise is too strong. We investigate in this paper an alternative solution, which builds on time distortion instead of spatial distortion. Specifically, our contribution lies in (1) the introduction of the concept of time distortion to anonymize mobility datasets (2) Promesse, a protection mechanism implementing this concept (3) a practical study of Promesse compared to two representative spatial distortion mechanisms, namely Wait For Me, which enforces k-anonymity, and Geo-Indistinguishability, which enforces differential privacy. We evaluate our mechanism practically using three real-life datasets. Our results show that time distortion reduces the number of points of interest that can be retrieved by an adversary to under 3 %, while the introduced spatial error is almost null and the distortion introduced on the results of range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Aug 2015, Helsinki, Finlan