CIS

Expressive cryptography, including Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE), has the potential to enable transformative new applications, drawing significant interest from industry. Unfortunately, it is often slow and resource-intensive, making those applications difficult to realize. For example, SMPC enables multiple organizations (e.g., hospitals) to run joint computations on their data (e.g., for […]

We will discuss methods of data lookup, with a focus on hash sets / tables, including motivation, properties, and variants. This will be in the context of a "CS 2" or data structures course. We will assume a basic familiarity with programming and comfort with implementing resizable lists; as well as knowledge of (but not […]

Humans exhibit a strong tendency to associate with those similar to them. This tendency, termed homophily in the social sciences, impacts both the structure of society and its outcomes. In this talk, Nicole Immorlica discusses the mathematics of homophily. She first quantifies its theoretical implications for geographic segregation. We will see that even tolerant societies […]

In the current digital and decentralized world, there is an imminent need for technologies that can provide a fast approach to compute on private data while guaranteeing secrecy. Secure Multiparty Computation (MPC) is one such cryptographic technology that provides an efficient approach to compute on private data. At a high level, MPC is a distributed […]

My research focuses on the security, privacy, and transparency of technologies in societal and legal context. My talk will focus on three of my recent works in this space, relating to (1) preventing exploitation of stolen email data, (2) enhancing accountability in electronic surveillance, and (3) legal risks faced by security researchers.

Technologies that enable confidential communication and anonymous authentication are important for improving privacy for users of internet services. Unfortunately, encryption and anonymity, while good for privacy, make it hard to hold bad actors accountable for misbehavior. Internet services rely on seeing message content to detect spam and other harmful content; services must also be able […]

Algorithms make predictions about people constantly.  The spread of such prediction systems has raised concerns that machine learning algorithms may exhibit problematic behavior, especially against individuals from marginalized groups.  This talk will provide an overview of my research building a theory of "responsible" machine learning.  I will highlight a notion of fairness in prediction, called […]