Paper 2025/1937

Noisy Function Secret Sharing and its applications to Differentially Private computations

Abstract

Function Secret Sharing (FSS) schemes enable to share secret functions between multiple parties, with notable applications in anonymous communication and privacy-preserving machine learning. While two-party schemes offer logarithmic key sizes, multi-party schemes remain less practical due to significantly larger keys. Although several approaches have been proposed to improve multi-party schemes, a significant efficiency gap remains between the two-party and multi-party settings. Our work introduces noisy FSS: a relaxation of FSS preserving the standard privacy guarantees but relaxing the correctness definition by allowing a small amount of noise in the output. We formally define noisy FSS and show how the noise introduced by the scheme can be leveraged to provide differential private outputs in statistics applications. To demonstrate the benefits of this relaxation, we adapt a scheme proposed by Corrigan-Gibbs et al. (S&P'15). While their scheme provides the smallest key sizes among multi-party schemes, they do not support some applications notably in statistics due to their non-linear share decoding. On the contrary, recent works such as Goel et al. (CRYPTO'25) have larger keys, but support all FSS applications. Our noisy adapted scheme offers the best of both worlds by matching the best key sizes, while providing the properties necessary to statistics applications.