How to Identify Anomalies Accurately and Privately

October 30, 2020 2:30 PM EDT via Zoom Webinar

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, I 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. I 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. 

Dartmouth's Institute for Security, Technology and Society (ISTS) invites you to join Dr. Vaidya in this timely and informative presentation.

To attend Dr. Vaidya's presentation use this link - 

https://dartmouth.zoom.us/j/95062738859?pwd=ekZWaTdUU2tWWEJvekRLNENZbnkrQT09

Passcode: 208899