Data-Driven Debugging for Functional Side Channels

Abstract

Information leaks through side channels are a pervasive problem in security-critical applications. Functional side channels arise when an attacker knows that the secret value of a server stays fixed for a certain time, and can observe the server executes on a sequence of different public inputs, each paired with the same secret input. Thus for each secret, the attackers observe a (partial) function from public values to (for instance) running time, and they can compare these functions for different secrets. First, we define a notion of noninterference for functional side channels. We focus on the case of noisy observations, where we demonstrate on examples that there is a practical functional side channel in programs that would be deemed information-leak-free using the standard definition. Second, we develop a framework and techniques for debugging programs for functional side channels. We adapt existing results and algorithms in functional data analysis (such as functional clustering) to discover the existence of side channels. We use a functional extension of standard decision tree learning to pinpoint the code fragments causing a side channel if there is one. Finally, we empirically evaluate the performance of our tool FUSCHIA on a series of micro-benchmarks, as well as on realistic Java programs with thousands of methods. FUSCHIA is able to discover (and locate in the code) functional side channels, including one that was since fixed by the original developers.

Quantitative Mitigation of Timing Side Channels

“…Side channel discovery. One can use existing tools to find the initial functional observations [44,45]. In Example 2.1, functional observations are F = y, 2y, .…”

“…We use the technique presented in [45] for the side channel discovery. The technique applies the functional data analysis [38] to create B-spline basis and fit functions to the vector of timing observations for each secret value.…”

“…First, we use the initial timing functions and characterize them with program internal properties such as basic block calls. To do so, we use the decision tree learning approach presented in [45]. The decision tree model characterizes each functional observations with properties of program internals.…”

“…Then, the developers noticed that the implementation in [6] can still leak information and fixed this issue with a new implementation in [5]. However, this new implementation is still leaking information [45]. We apply Schmit to mitigate this timing side channels.…”

“…We develop our tool Schmit that has three components: side channel discovery [45], search for the mitigation policy, and the policy enforcement. The side channel discovery builds the functional observations [45] and measures the entropy of secret set after the observations. The mitigation policy component includes the implementation of the dynamic programming and optimization algorithms.…”