An Analysis of the Learning Parity with Noise Assumption Against Fault Attacks

We provide a rst security evaluation of LPN-based implementations against fault attacks. Our main result is to show that such implementations inherently have good features to resist these attacks. First, some prominent fault models (e.g. where an adversary flips bits in an implementation) are ineffective against LPN. Second, attacks taking advantage of more advanced fault models (e.g. where an adversary sets bits in an implementation) require significantly more samples than against standard symmetric cryptographic primitives such as block ciphers. Furthermore, the sampling complexity of these attacks strongly suers from inaccurate fault insertion. Combined with the previous observation that the inner products computed in LPN implementations have an interesting algebraic structure for side-channel resistance via masking, these results therefore suggest LPN-based primitives as interesting candidates for physically secure implementations