In the era of pervasive data collection and AI-driven decision making, ensuring both privacy and trust in machine learning systems is more critical than ever. This talk explores how we can build AI/ML systems that are not only effective but also respectful of individual privacy and accountable in their decision-making. Drawing on a wide range of real-world applications, from pandemic surveillance to financial anomaly detection, we delve into recent advances in privacy-preserving techniques, including federated learning, differential privacy, secure multiparty computation, and synthetic data generation. We also introduce the concept of Sensitive Privacy, a novel approach for protecting anomalous records, and discuss how these innovations can be practically implemented to support secure, equitable, and trustworthy AI. By grounding the discussion in real-world systems and interdisciplinary collaboration, we aim to provide a roadmap for building AI/ML systems that are both trustworthy and private.
Bio:
Jaideep Vaidya is a Distinguished Professor of Computer Information Systems at Rutgers University and the Vice Dean for Faculty Affairs and Research at Rutgers Business School – Newark and New Brunswick. He is also the Director of the Rutgers Institute for Data Science, Learning, and Applications. His research focuses on the intersection of privacy, security, data mining, data management, and artificial intelligence, with a strong emphasis on real-world applications and interdisciplinary impact. He has authored over 200 peer-reviewed publications and received best paper awards across leading venues in data mining, databases, digital government, cybersecurity, and healthcare informatics. He is a Fellow of the AAAS, AAIA, ACMI, AIMBE, IAHSI, IEEE, and IFIP, and an ACM Distinguished Scientist. He served as Editor-in-Chief of the IEEE Transactions on Dependable and Secure Computing and is currently the Editor-in-Chief of the ACM Transactions on Internet Technology.