CryptoDB
Side-Channel Masking with Common Shares
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Abstract: | To counter side-channel attacks, a masking scheme randomly encodes keydependent variables into several shares, and transforms operations into the masked correspondence (called gadget) operating on shares. This provably achieves the de facto standard notion of probing security.We continue the long line of works seeking to reduce the overhead of masking. Our main contribution is a new masking scheme over finite fields in which shares of different variables have a part in common. This enables the reuse of randomness / variables across different gadgets, and reduces the total cost of masked implementation. For security order d and circuit size l, the randomness requirement and computational complexity of our scheme are Õ(d2) and Õ(ld2) respectively, strictly improving upon the state-of-the-art Õ(d2) and Õ(ld3) of Coron et al. at Eurocrypt 2020.A notable feature of our scheme is that it enables a new paradigm in which many intermediates can be precomputed before executing the masked function. The precomputation consumes Õ(ld2) and produces Õ(ld) variables to be stored in RAM. The cost of subsequent (online) computation is reduced to Õ(ld), effectively speeding up e.g., challenge-response authentication protocols. We showcase our method on the AES on ARM Cortex M architecture and perform a T-test evaluation. Our results show a speed-up during the online phase compared with state-of-the-art implementations, at the cost of acceptable RAM consumption and precomputation time.To prove security for our scheme, we propose a new security notion intrinsically supporting randomness / variables reusing across gadgets, and bridging the security of parallel compositions of gadgets to general compositions, which may be of independent interest. |
BibTeX
@article{tches-2022-32068, title={Side-Channel Masking with Common Shares}, journal={IACR Transactions on Cryptographic Hardware and Embedded Systems}, publisher={Ruhr-Universität Bochum}, volume={2022, Issue 3}, pages={290-329}, url={https://tches.iacr.org/index.php/TCHES/article/view/9703}, doi={10.46586/tches.v2022.i3.290-329}, author={Weijia Wang and Chun Guo and Yu Yu and Fanjie Ji and Yang Su}, year=2022 }