Title: Algorithmic Stability for Trustworthy Machine Learning and Statistics
Abstract: Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these risks—and efficient in their use of resources is essential for enabling trustworthy data-driven systems. In this talk, Lydia Zakynthinou will focus on statistical estimation under differential privacy—a rigorous framework that ensures data-driven system outputs do not reveal sensitive information about individuals in their input. She will present algorithmic techniques that take advantage of beneficial structure in the data to achieve optimal error for several multivariate tasks without requiring any prior information about the data by building on robustness against data poisoning attacks. Lastly, Zakynthinou will highlight the deeper connection between differential privacy and robustness that underpins these results.
Bio: Lydia Zakynthinou is a Foundations of Data Science Institute postdoctoral research fellow in the Simons Institute for the Theory of Computing at the University of California, Berkeley, hosted by Michael I. Jordan. Zakynthinou earned her PhD in computer science from Northeastern University under the supervision of Jonathan Ullman and Huy Nguyen. Her research lies in trustworthy machine learning and statistics, with a focus on data privacy and generalization, and has been recognized with a Meta Research PhD Fellowship and a Khoury College PhD Research Award. Zakynthinou holds a diploma in electrical and computer engineering from the National Technical University of Athens and an MSc in logic, algorithms, and theory of computation from the National and Kapodistrian University of Athens in Greece.
Title: Building Haystacks to Find Needles
Abstract: The internet is a big place, comprising billions of users and tens of billions of network devices. Discovering and remediating vulnerabilities in these devices is an imperative for a more secure internet. Unfortunately, vulnerabilities that affect millions of hosts represent only a small fraction of the overall internet. Finding these “needles” at internet scale requires collecting an exponentially larger “haystack.” In this talk, Erik Rye will describe two novel techniques he developed to collect unprecedentedly large network datasets. He will describe how he used these datasets to enable the discovery of new network security and privacy problems at internet scale. These include stark, real-world security and privacy vulnerabilities, such as revealing troop positions in Ukraine and exposing previously-unreachable Internet of Things devices like smart light bulbs in users’ homes. Rye’s findings have prompted design changes in systems run by Apple, SpaceX, and router manufacturers, and improved the security and privacy of millions of affected individuals.
Bio: Erik Rye is a final-year PhD candidate at the University of Maryland, where he focuses on solving large-scale network security and privacy problems. He regularly publishes in venues like the ACM Special Interest Group on Data Communications Conference and IEEE Security & Privacy, and he has shared his work at industry conventions like Black Hat USA and in popular media like KrebsOnSecurity.com. Rye contributes to the network security and measurement communities by running the IPv6 Observatory, which publishes weekly insights into the state of the internet. He holds master’s degrees in computer science and applied mathematics from the Naval Postgraduate School, and also likes dogs.
