Quantitative Mitigation of Timing Side Channels

TL;DR

This paper introduces a novel approach called SCHMIT for mitigating timing side channels by reducing information leaks through entropy-based objectives, while maintaining user-defined performance overhead, applicable to real-world applications.

Contribution

It formulates the problem of timing side channel mitigation as Shannon mitigation, providing polynomial algorithms for deterministic cases and optimization-based solutions for stochastic cases, addressing a gap in existing methods.

Findings

SCHMIT effectively reduces timing side channels in benchmarks

The approach guarantees confidentiality within performance bounds

It scales to real-world applications with functional timing observations

Abstract

Timing side channels pose a significant threat to the security and privacy of software applications. We propose an approach for mitigating this problem by decreasing the strength of the side channels as measured by entropy-based objectives, such as min-guess entropy. Our goal is to minimize the information leaks while guaranteeing a user-specified maximal acceptable performance overhead. We dub the decision version of this problem Shannon mitigation, and consider two variants, deterministic and stochastic. First, we show the deterministic variant is NP-hard. However, we give a polynomial algorithm that finds an optimal solution from a restricted set. Second, for the stochastic variant, we develop an algorithm that uses optimization techniques specific to the entropy-based objective used. For instance, for min-guess entropy, we used mixed integer-linear programming. We apply the algorithm to a threat model where the attacker gets to make functional observations, that is, where she observes the running time of the program for the same secret value combined with different public input values. Existing mitigation approaches do not give confidentiality or performance guarantees for this threat model. We evaluate our tool SCHMIT on a number of micro-benchmarks and real-world applications with different entropy-based objectives. In contrast to the existing mitigation approaches, we show that in the functional-observation threat model, SCHMIT is scalable and able to maximize confidentiality under the performance overhead bound.

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