Emiliano De Cristofaro

TL;DR: MAMADROID as discussed by the authors is a static analysis-based system that abstracts API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains.

TL;DR: This paper begins to address genomic privacy by focusing on three important applications: Paternity Tests, Personalized Medicine, and Genetic Compatibility Tests, and proposes a set of efficient techniques based on private set operations that allow in in silico some operations that are currently performed via in vitro methods, in a secure fashion.

TL;DR: In this article, the authors present a game-based definition of membership inference on aggregate location time-series, and cast it as a classification problem where machine learning can be used to distinguish whether or not a target user is part of the aggregates.

TL;DR: In this paper, a set of efficient techniques based on private set operations is proposed for paternity tests, personalized medicine, and genetic compatibility tests, which can be implemented in in silico some operations that are currently performed via in vitro methods in a secure fashion.

TL;DR: In this paper, the problem of computing the magnitude, rather than the contents, of the intersection is considered, and the problem is referred to as Private Set Intersection Cardinality (PSI-CA).