This paper explores advancements in the open-source library, Private Computation Framework, that significantly improve the resource usage and application development lifecycle of...
In this paper, we report on some of the unique challenges when analyzing CMake files at Facebook.
In this work, we provide an in-depth characterization study of the performance overhead for running Transformer models with secure multi-party computation (MPC). MPC is a...
This paper presents a quantitative and qualitative analysis of the top submissions. It appears that the most difficult image transformations involve either severe image crops...
In this work we review a number of existing proposals for distributed payment systems that offer some form of auditability for regulators.
In this work, we argue that membership inference attacks can benefit drastically from difficulty calibration, where an attack’s predicted membership score is adjusted to the...
We introduce FAUSTA, an algorithmic traffic generation platform that enables analysis and testing at scale. FAUSTA has been deployed at Meta to analyze and test the WhatsApp platform infrastructure since September 2020, enabling WhatsApp developers to deploy reliable code changes to a code base of millions of lines of code, supporting over 2 billion users who rely on WhatsApp for their daily communications.
We consider privacy in the context of streaming algorithms for cardinality estimation. We show that a large class of algorithms all satisfy epsilon-differential privacy, so long as...
We consider overparameterized networks in high dimensions with quadratic targets and infinite samples. Our analysis allows us to identify new trade-offs between generalization and...
As a starting point, we evaluated our solution against the widely-used secp family of elliptic curves and show that we can achieve storage reduction by 7x-14x, depending on the group size.
