We introduce a new rank-based key encapsulation mechanism (KEM) with public key and ciphertext sizes around 3.5 Kbytes each, for 128 bits of security, without using ideal structures. Such structures allow to compress objects, but give reductions to specific problems whose security is potentially weaker than for unstructured problems. To the best of our knowledge, our scheme improves in size all the existing unstructured post-quantum lattice or code-based algorithms such as FrodoKEM or Classic McEliece. Our technique, whose efficiency relies on properties of rank metric, is to build upon existing Low Rank Parity Check (LRPC) code-based KEMs and to send multiple syndromes in one ciphertext, allowing to reduce the parameters and still obtain an acceptable decoding failure rate. Our system relies on the hardness of the Rank Support Learning problem, a well-known variant of the Rank Syndrome Decoding problem. The gain on parameters is enough to significantly close the gap between ideal and non-ideal constructions. It enables to choose an error weight close to the rank Gilbert-Varshamov bound, which is a relatively harder zone for algebraic attacks. We also give a version of our KEM that keeps an ideal structure and permits to roughly divide the bandwidth by two compared to previous versions of LRPC KEMs submitted to the NIST with a Decoding Failure Rate (DFR) of $2^{-128}$.
翻译:我们引入了新的基于等级的钥匙封装机制(KEM), 公用钥匙和密码大小约为每约3.5千兆字节, 使用128位安全, 不使用理想结构 。 这种结构允许压缩对象, 但减少安全可能比非结构化问题更弱的特定问题。 根据我们所知, 我们的计划在规模上改进了所有现有的非结构化后Qantuum Lattice 或基于代码的算法, 如FrodoKEM 或经典的McELEliece 。 我们的技术, 其效率取决于等级衡量的特性, 是利用现有的低级Pity检查( LPC) 代码 KEMMs 技术, 并且用一个加密文本发送多重综合症, 能够减少参数, 但仍然获得可接受的解码失败率。 我们的系统依赖于排名支持学习问题的难度, 一种众所周知的兰氏综合症变异体。 参数的增益足以大大缩小理想与非理想结构之间的差距。 它能够选择接近等级R$( LERP) 的重量, 基尔伯特- Vamebelberlus 的平面结构, 也让前KEMov 更难等级结构成为我们更难的KEMov 。