CORE

User authentication is an important part of most information systems that require some level of security. Due to their ease of use, wide deployment, and user familiarity, passwords have been the most widely adopted user authentication mechanism in the past and are likely to continue to be an important part of cybersecurity for the foreseeable future. At the same time, it is well known that there is a tension between the security and usability of passwords.

Malicious programs ("malware") are expensive and can put people's lives at risk. Unfortunately, automatic malware detection is difficult and many automated detection systems produce a large number of false alarms. In large enterprises, detectors may create millions of security log entries per day, deluging the human analysts with false alarms. This project is developing algorithmic and statistical techniques to automatically analyze these security logs and reduce the number that human analysts must review from millions to only tens or hundreds per day.

Increasingly, decisions and actions affecting people's lives are determined by automated systems processing personal data. Excitement about these systems has been accompanied by serious concerns about their opacity and the threats that they pose to privacy, fairness, and other values. Recognizing these concerns, the investigators seek to make real-world automated decision-making systems accountable for privacy and fairness by enabling them to detect and explain violations of these values. The technical work is informed by, and applied to, online advertising, healthcare, an