In this paper we describe a carefully crafted approach that directly models the central aspects of smart contracts natively, going from the contract to its logical representation...
In this work, we consider the formal verification of the public-key encryption scheme of Saber, one of the selected few post-quantum cipher suites currently considered for potential...
We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP). Most prior work on this problem is restricted to the case where...
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows...
We study the problem of robustly estimating the parameter p of an Erdős-Rényi random graph on n nodes, where a γ fraction of nodes may be adversarially corrupted.
In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accuracy.
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...
