Learning with Errors is one of the fundamental problems in computational learning theory and has in the last years become the cornerstone of post-quantum cryptography. In this work, we study the quantum sample complexity of Learning with Errors and show that there exists an efficient quantum learning algorithm (with polynomial sample and time complexity) for the Learning with Errors problem where the error distribution is the one used in cryptography. While our quantum learning algorithm does not break the LWE-based encryption schemes proposed in the cryptography literature, it does have some interesting implications for cryptography: first, when building an LWE-based scheme, one needs to be careful about the access to the public-key generation algorithm that is given to the adversary; second, our algorithm shows a possible way for attacking LWE-based encryption by using classical samples to approximate the quantum sample state, since then using our quantum learning algorithm would solve LWE. Finally, we extend our results and show quantum learning algorithms for three related problems: Learning Parity with Noise, Learning with Rounding and Short Integer Solution.

We present various FPGA implementations of protections against SCAs for RLWE-based PKE. We implemented the main solutions from the state of the art with improved variants. We also propose a new protection based on a redundant representation of the ring elements to randomize computations. We compare the implementation results of all these solutions.

This paper deals with hardware implementations for lattice-based cryptography. Various CPA and CCA secure algorithms for LWE, RLWE and MLWE problems have been studied, parallelized, implemented and compared on FPGA using high-level synthesis. The impact of PRNG choices on the implementations performances and costs is also evaluated. HLS allows us to compare various sets of algorithms, architectures and parameters with a reduced design effort. Our results are often similar to state-of-the-art for various speed and cost trade-offs. Sometimes we obtain better results thanks to the exploration of numerous architecture and algorithm optimizations.