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Abstract. We present a new algorithm for solving the LPN problem. The algorithm has a similar form as some previous methods, but includes a new key step that makes use of approximations of random words to a nearest codeword in a linear code. It outperforms previous methods for many parameter choices. In particular, we can now solve instances suggested for 80-bit security in cryptographic schemes like HB variants, LPN-C and Lapin, in less than 2 80 operations.

In this paper, we present a side-channel attack on a first-order masked implementation of IND-CCA secure Saber KEM. We show how to recover both the session key and the long-term secret key from 24 traces using a deep neural network created at the profiling stage. The proposed message recovery approach learns a higher-order model directly, without explicitly extracting random masks at each execution. This eliminates the need for a fully controllable profiling device which is required in previous attacks on masked implementations of LWE/LWR-based PKEs/KEMs. We also present a new secret key recovery approach based on maps from error-correcting codes that can compensate for some errors in the recovered message. In addition, we discovered a previously unknown leakage point in the primitive for masked logical shifting on arithmetic shares.