We enumerate these challenges and provide solutions to address them. In particular, we design and implement a memory-optimized and privacy-preserving verifiable data structure...
We propose Accountable JS, a browser extension and opt-in protocol for accountable delivery of active content on a web page. We prototype our protocol, formally model its...
We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently...
The ever-increasing adoption of privacy-enhancing technologies (PETs) provides new layers of privacy in areas such as secure data analytics and machine learning. The first step...
In August, Meta launched the 2022 People’s Expectations and Experiences with Digital Privacy request for proposals (RFP). Today, it is my sincere honor to announce the winners...
Robust modules guarantee to do only what they are supposed to do – even in the presence of untrusted, malicious clients, and considering not just the direct behavior of...
In this work, we propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic dataset...
In this work, we train transformers to perform modular arithmetic and mix half-trained models with statistical cryptanalysis techniques to propose SALSA: a machine learning...
In this work, we implement the GBDT model under Differential Privacy (DP). We propose a general framework that captures and extends existing approaches for differentially...
In June, WhatsApp launched its first research award opportunity on privacy-aware program analysis. Today, we’re announcing the winners of that award.
