The widespread adoption of deep learning is facilitated in part by the availability of large-
scale data for training desirable models. However, these data may involve sensitive …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

Machine learning models are vulnerable to data inference attacks, such as membership
inference and model inversion attacks. In these types of breaches, an adversary attempts to …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

The proliferation of edge computing and the Internet of Vehicles (IoV) has significantly
bolstered the popularity of deep learning-based driver assistance applications. This has …

We propose Agora, the first blockchain-based data marketplace that enables multiple
privacy-concerned parties to get compensated for contributing and exchanging data, without …

Private learning of top-k, ie, the k most frequent values also called heavy hitters, is a
common industry scenario: Companies want to privately learn, eg, frequently typed new …

The emergence of new machine learning methods has led to their widespread application
across various domains, significantly advancing the field of artificial intelligence. However …