Increasing affordability of genome sequencing and, as a consequence, widespread availability genomic data opens up new opportunities for the field medicine, also evident from emergence popular cloud-based offerings in this area, such Google Genomics [1]. To utilize more efficiently, it is crucial that different entities share their with each other. However, sharing risky mainly due to privacy c...

In this paper we illustrate a privacy framework named Indistinguishable Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, diffe...

Differential privacy (DP) is a well-studied notion of privacy that is generally achieved by randomizing outputs to preserve the privacy of the input records. A central problem in differential privacy is how much accuracy must be lost in order to preserve input privacy? Our work obtains general upper bounds on accuracy for differentially private two-party protocols computing any Boolean function...

We study Differential Privacy in the abstract setting of Probability on metric spaces. Numerical, categorical and functional data can be handled in a uniform manner in this setting. We demonstrate how mechanisms based on data sanitisation and those that rely on adding noise to query responses fit within this framework. We prove that once the sanitisation is differentially private, then so is th...

In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptively, as a function of the outcome of previously run analyses. This case is much more delicate than the setting covered by existing composition theorems, in which the algorithms themselves can be chosen adaptively, but ...

The mobile Internet is a fast growing technology that introduces new privacy risks. We argue that, since privacy legislation alone is not sufficient to protect the user’s privacy, technical solutions to enhance informational privacy of individuals are also needed. This paper introduces mCrowds, a privacy-enhancing technology that combines the concept of a crowd system in a mobile Internet setti...

The mining of frequent patterns is a fundamental component in many data mining tasks. A considerable amount of research on this problem has led to a wide series of efficient and scalable algorithms for mining frequent patterns. However, releasing these patterns is posing concerns on the privacy of the users participating in the data. Indeed the information from the patterns can be linked with a...

Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of i...