Emiliano De Cristofaro

TL;DR: In this article, passive and active inference attacks are proposed to exploit the leakage of information about participants' training data in federated learning, where each participant can infer the presence of exact data points and properties that hold only for a subset of the training data and are independent of the properties of the joint model.

TL;DR: This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.

TL;DR: The authors proposed a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users, finding that bullies are relatively popular and tend to include more negativity in their posts.

TL;DR: In this paper, membership inference attacks against generative models are presented, where given a data point, the adversary determines whether or not it was used to train the model, and the attacks leverage Generative Adversarial Networks (GANs), which combine a discriminative and a generative model, to detect overfitting and recognize inputs that were part of training datasets, using the discriminator's capacity to learn statistical differences in distributions.

TL;DR: MAMADROID as mentioned in this paper is an Android malware detection system that relies on app behavior and builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app and uses it to extract features and perform classification.