Paper 2025/371

Functional Oblivious Transfer with Applications in Privacy-Preserving Machine Learning

Abstract

Oblivious Transfer (OT) is a fundamental cryptographic primitive introduced nearly four decades ago. OT allows a receiver to select and learn $t$ out of $n$ private messages held by a sender. It ensures that the sender does not learn which specific messages the receiver has chosen, while the receiver gains no information about the remaining $n-t$ messages. In this work, we introduce the notion of functional OT (FOT), for the first time. FOT adds a layer of security to the conventional OT by ensuring that the receiver only learns a function of the selected messages rather than the $t$ individual messages themselves. We propose several protocols that realize this concept. In particular, we propose concrete instantiations of FOT when the function to be executed on the selected message is mean, mode, addition, or multiplication. The schemes are efficient and unconditionally secure. We also propose a non-trivial protocol that supports arbitrary functions on the selected messages mainly using fully homomorphic encryption (FHE) and oblivious linear function evaluation, where the number of FHE invocations is constant $$ with respect to $$. Our asymptotic and concrete cost analyses demonstrate the efficiency of our unconditionally secure FOT protocols. FOT can enhance the security of privacy-preserving machine learning, particularly in (i) K-Nearest Neighbors schemes and (ii) client selection in Federated Learning (FL).