The rise of connected personal devices together with privacy concerns call for machine
learning algorithms capable of leveraging the data of a large number of agents to learn …

Bug-fixes are widely used for predicting bugs or finding risky parts of software. However, a
bug-fix does not contain information about the change that initially introduced a bug. Such …

The Internet of Things equips citizens with a phenomenal new means for online participation
in sharing economies. When agents self-determine options from which they choose, for …

The massive collection of personal data by personalization systems has rendered the
preservation of privacy of individuals more and more difficult. Most of the proposed …

EasyCrypt is a tool-assisted framework for reasoning about probabilistic computations in the
presence of adversarial code, whose main application has been the verification of security …

The pervasiveness of Internet of Things devices in techno-socio-economic domains such as
Smart Cities and Smart Grids results in a massive scale of data about our society. Decision …

Differential Privacy has recently emerged as a measure for protecting privacy in distorted
data. While this seems to solve many problems, in practice it still leaves a number of security …

Jaccard Similarity has been widely used to measure the distance between two sets (or
preference profiles) owned by two different users. Yet, in the private data collection scenario …

We revisit the input perturbations framework for differential privacy where noise is added to
the input $ A\in\mathcal {S} $ and the result is then projected back to the space of admissible …

In many industrial applications of big data, the Jaccard Similarity Computation has been
widely used to measure the distance between two profiles or sets respectively owned by two …