Output-sensitive Information flow analysis

TL;DR

This paper introduces an output-sensitive noninterference approach for verifying constant-time programming, allowing safe relaxation of strict policies by considering public outputs and tracking information flow more precisely.

Contribution

It proposes a novel type-based method that accounts for both initial inputs and final outputs to improve information flow analysis in constant-time programming.

Findings

Successfully adapted to LLVM IR

Prototype verifies LLVM implementations effectively

Tracks dependence on both inputs and outputs

Abstract

Constant-time programming is a countermeasure to prevent cache based attacks where programs should not perform memory accesses that depend on secrets. In some cases this policy can be safely relaxed if one can prove that the program does not leak more information than the public outputs of the computation. We propose a novel approach for verifying constant-time programming based on a new information flow property, called output-sensitive noninterference. Noninterference states that a public observer cannot learn anything about the private data. Since real systems need to intentionally declassify some information, this property is too strong in practice. In order to take into account public outputs we proceed as follows: instead of using complex explicit declassification policies, we partition variables in three sets: input, output and leakage variables. Then, we propose a typing system to statically check that leakage variables do not leak more information about the secret inputs than the public normal output. The novelty of our approach is that we track the dependence of leakage variables with respect not only to the initial values of input variables (as in classical approaches for noninterference), but taking also into account the final values of output variables. We adapted this approach to LLVM IR and we developed a prototype to verify LLVM implementations.