Optimal Accuracy-Privacy Trade-Off for Secure Multi-Party Computations
This work addresses privacy concerns in multi-party computations for applications like secure data analysis, though it is incremental as it builds on existing entropy measures and substitution concepts.
The paper tackles the problem of information leakage in Secure Multi-Party Computation by proposing a measure to quantify leakage and introducing function substitution to enhance confidentiality, demonstrating theoretical bounds and experimental improvements in privacy while controlling output distortion.
The purpose of Secure Multi-Party Computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output of such computations inevitably reveals some information about the private inputs. We propose a measure generalising both Renyi entropy and g-entropy so as to quantify this information leakage. In order to control and restrain such information flows, we introduce the notion of function substitution which replaces the computation of a function that reveals sensitive information with that of an approximate function. We exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values. Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realise such optimal trade-offs.