Paper 2019/1302

There Is Always an Exception: Controlling Partial Information Leakage in Secure Computation

Máté Horváth, Levente Buttyán, Gábor Székely, and Dóra Neubrandt

Abstract

Private Function Evaluation (PFE) enables two parties to jointly execute a computation such that one of them provides the input while the other chooses the function to compute. According to the traditional security requirements, a PFE protocol should leak no more information, neither about the function nor the input, than what is revealed by the output of the computation. Existing PFE protocols inherently restrict the scope of computable functions to a certain function class with given output size, thus ruling out the direct evaluation of such problematic functions as the identity map, which would entirely undermine the input privacy requirement. We observe that when not only the input $x$ is confidential but certain partial information $g(x)$ of it as well, standard PFE fails to provide meaningful input privacy if $g$ and the function $f$ to be computed fall into the same function class. Our work investigates the question whether it is possible to achieve a reasonable level of input and function privacy simultaneously even in the above cases. We propose the notion of Controlled PFE (CPFE) with different flavours of security and answer the question affirmatively by showing simple, generic realizations of the new notions. Our main construction, based on functional encryption (FE), also enjoys strong reusability properties enabling, e.g. fast computation of the same function on different inputs. To demonstrate the applicability of our approach, we show a concrete instantiation of the FE-based protocol for inner product computation that enables secure statistical analysis (and more) under the standard Decisional Diffie--Hellman assumption.