Paper 2024/1035

Reading It like an Open Book: Single-trace Blind Side-channel Attacks on Garbled Circuit Frameworks

Abstract

Garbled circuits (GC) are a secure multiparty computation protocol that enables two parties to jointly compute a function using their private data without revealing it to each other. While garbled circuits are proven secure at the protocol level, implementations can still be vulnerable to side-channel attacks. Recently, side-channel analysis of GC implementations has garnered significant interest from researchers. We investigate popular open-source GC frameworks and discover that the AES encryption used in the garbling process follows a secret-dependent sequence. This vulnerability allows private inputs to be exposed through side-channel analysis. Based on this finding, we propose a side-channel attack on garbled circuits to recover the private inputs of both parties. Our attack does not require access to any plaintexts or ciphertexts in the protocol and is single-trace, adhering to the constraint that a garbled circuit can be executed only once. Furthermore, unlike existing attacks that can only target input non-XOR gates, our method applies to both input and internal non-XOR gates. Consequently, the secrets associated with every non-XOR gate are fully exposed as in an open book. We comprehensively evaluate our attack in various scenarios. First, we perform the attack on single-platform software implementations of standard AES and interleaved AES on a 32-bit ARM processor, achieving a $100\%$ success rate in both cases. Next, we target a hardware implementation on a Xilinx Artix-7 FPGA, where the resolution of power consumption measurements and the number of samples are significantly limited. In this scenario, our attack achieves a success rate of $79.58\%$. Finally, we perform a cross-platform attack on two processors with different microarchitectures representing the two parties. The differing execution cycles and power sensors across the platforms increase the difficulty of side-channel analysis. Despite these challenges, our point-of-interest (POI) selection method allows our attack to achieve a $100\%$ success rate in this scenario as well. We also discuss effective countermeasures that can be readily applied to GC frameworks to mitigate this vulnerability.