CS Events Monthly View
Computer Science Department Colloquium
How to identify anomalies accurately and privately
Thursday, October 21, 2021, 11:00am
Speaker: Professor Jaideep Vaidya
Jaideep Vaidya is a Professor in the MSIS Department at Rutgers University. He received the B.E. degree in Computer Engineering from the University of Mumbai, the M.S. and Ph.D. degree in Computer Science from Purdue University. His general area of research is in security, privacy, data mining, and data management. He has published over 190 technical papers in peer-reviewed journals and conference proceedings, and has received several best paper awards from the premier conferences in data mining, databases, digital government, security, and informatics. He is an ACM Distinguished Scientist, and IEEE Fellow, and is the Editor in Chief of the IEEE Transactions on Dependable and Secure Computing.
Location : Via Zoom
Event Type: Computer Science Department Colloquium
Abstract: In the current digital age, data is continually being collected by organizations and governments alike. While the goal is to use this data to derive insight and improve services, the ubiquitous collection and analysis of data creates a threat to privacy. In this talk, we examine the problem of private anomaly identification. Anomaly detection is one of the most fundamental data analysis tasks, and is useful in applications as far ranging as homeland security, to medical informatics, to financial fraud. However, many applications of outlier detection such as detecting suspicious behavior for counter-terrorism or anti-fraud purposes also raise privacy concerns. We conclusively demonstrate that differential privacy (the de facto model for privacy used today) is inherently incapable of solving this problem. We then present a new notion of privacy, called Sensitive Privacy, that protects the vast majority of records that are or could be normal, while still enabling accurate identification of records that are anomalous. Given the widespread impact of COVID-19, we also present some results from a recent NSF funded effort to perform privacy-preserving crowdsensing of COVID-19, from the context of hotspot detection.
Rutgers University School of Arts and Sciences
Contact Host: Professor Dimitri Metaxas