How can a model owner prove they trained their model according to the correct
specification? More importantly, how can they do so while preserving the privacy of the …

The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and
been widely applied in a variety of applications, including FPGAs, image processing, and …

Abstract Secure 2-party computation (2PC) enables secure inference that offers protection
for both proprietary machine learning (ML) models and sensitive inputs to them. However …

Deep neural network (DNN) inference based on secure 2-party computation (2PC) can offer
cryptographically-secure privacy protection but suffers from orders of magnitude latency …

Softmax and sigmoid, composing exponential functions (ex) and division (1/x), are activation
functions often required in training. Secure computation on non-linear, unbounded 1/x and …

Secure 2-party computation (2PC) of floating-point arithmetic is improving in performance
and recent work runs deep learning algorithms with it, while being as numerically precise as …

The advent of transformers has brought about significant advancements in traditional
machine learning tasks. However, their pervasive deployment has raised concerns about …

Abstract Secure Two-party Computation (2PC) allows two parties to compute any function on
their private inputs without revealing their inputs to each other. In the offline/online model for …

We introduce HELiKs, a groundbreaking framework for fast and secure matrix multiplication
and 3D convolutions, tailored for privacy-preserving machine learning. Leveraging …

We propose a novel framework, Manticore, for multiparty computations, with full threshold
and semi-honest security model, supporting a combination of real number arithmetic …