Division of Computer and Network Systems (CNS)

Data-driven technology is having an impressive impact on society but privacy concerns restrict the way data can be used and released. Differential privacy has emerged as a leading notion supporting efficient and accurate data analyses that respect privacy. But designing and implementing efficient differentially private data analyses with high utility can be challenging and error prone. Even privacy experts have released code with bugs or designed incorrect algorithms.

Social and behavioral research has a long history of collecting digitized data. With more and more studies incorporating participants' smartphone sensor and usage data, individual privacy has become a serious concern. Although researchers are trained to exercise confidentiality, sensitive information can be inferred from the smartphone data about the participants and their environment (e.g., families and communities). Without a systematic solution to address privacy concerns, participants may withdraw and request removal of their data from studies.

Code injection vulnerabilities are a class of security vulnerabilities that have been exploited increasingly often, including in the high-profile 2017 Equifax breach as well as in many recent attacks on our country's election and financial systems. These vulnerabilities are very tricky to detect, and there are no existing automated techniques to protect critical software from being released with these dangerous flaws. This project is developing new and transformative approaches for detecting code injection vulnerabilities in complex, large-scale systems.

Network and Security Operations Centers (SOCs) are central components of modern enterprise networks. SOCs manage network operations, defend against cyber threats, and maintain regulatory compliance. Typically, management and SOC operators use monitoring software and metrics, such as open and closed tickets, to manage SOC efficiency. These metrics may fail to represent the real effectiveness of the SOC and the security posture of the network.

Individuals generate enormous amounts of personal data that are subsequently collected and stored by organizations and governments. The data powers many innovative applications in areas such as web services, health care, and transportation, but they also increase privacy risks. Differential privacy, a framework to rigorously reason about privacy properties of algorithms, holds tremendous promise for enabling privacy-preserving yet useful data analyses. However, its adoption has been limited to entities with massive user bases.