J
Jordi Soria-Comas
Researcher at Rovira i Virgili University
Publications - 45
Citations - 1264
Jordi Soria-Comas is an academic researcher from Rovira i Virgili University. The author has contributed to research in topics: Differential privacy & Information privacy. The author has an hindex of 17, co-authored 45 publications receiving 982 citations.
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Enhancing data utility in differential privacy via microaggregation-based $$k$$k-anonymity
TL;DR: It is shown that the amount of noise required to fulfill $$\varepsilon $$ε-differential privacy can be reduced if noise is added to a $$k$$k-anonymous version of the data set, where k-anonymity is reached through a specially designed microaggregation of all attributes.
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Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
TL;DR: In this article, the authors argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks, and they propose an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals).
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t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
TL;DR: In this paper, a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases is proposed and evaluated. But it does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of $k$ subjects is too small.
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Big Data Privacy: Challenges to Privacy Principles and Models
TL;DR: How well the two main privacy models used in anonymization meet the requirements of big data, namely composability, low computational cost and linkability is evaluated.
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Optimal data-independent noise for differential privacy
TL;DR: This work proposes a general optimality criterion based on the concentration of the probability mass of the noise distribution around zero, and shows that any noise optimal under this criterion must be optimal under any other sensible criterion.