Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in
machine learning applications that has alluded to pivotal debates in recent years. Such …

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy
risks in machine learning. However, exactly how small the privacy parameter $\epsilon …

As Video Streaming and Analytics (VSA) systems become increasingly popular, serious
privacy concerns have risen on exposing too much unnecessary private information to the …

Cloud-based machine learning inference is an emerging paradigm where users query by
sending their data through a service provider who runs an ML model on that data and …

We investigate privacy-preserving, video-based action recognition in deep learning, a
problem with growing importance in smart camera applications. A novel adversarial training …

The central goal of Algorithmic Fairness is to develop AI-based systems which do not
discriminate subgroups in the population with respect to one or multiple notions of inequity …

Recent deep learning models have shown remarkable performance in image classification.
While these deep learning systems are getting closer to practical deployment, the common …

Federated learning (FedL) has emerged as a popular technique for distributing model
training over a set of wireless devices, via iterative local updates (at devices) and global …

Privacy-preserving instance encoding aims to encode raw data into feature vectors without
revealing their privacy-sensitive information. When designed properly, these encodings can …

There are demographic biases present in current facial recognition (FR) models. To
measure these biases across different ethnic and gender subgroups, we introduce our …