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Ring Learning With Errors (RLWE)-based key exchange is one of the most efficient and secure primitive for post-quantum cryptography. One common approach to achieve key exchange over RLWE is error reconciliation. Recently, an efficient attack against reconciliation-based RLWE key exchange protocols with reused keys was proposed. This attack can recover a long-term private key if a key pair is reused. We also know that in the real world, key reuse is commonly adopted in applications like the Transport Layer Security (TLS) protocol to improve performance. Directly motivated by this attack, we construct a new randomized RLWE-based key exchange protocol against this attack. Our lightweight approach incorporates an additional ephemeral public error term into key exchange materials, so that this attack no longer works. With the same attack, we practically show that the signal value of our protocol is indistinguishable from uniform random, therefore, this attack no longer works. We explain how the attack fails, present 200-bit classic and 80-bit quantum secure parameter choice, efficient implementations, comparisons and discussion. Benchmark shows our protocol is truly efficient and even faster than related vulnerable protocols.

3D shape classification plays a fundamental role in geometric big data analysis. This work proposes a novel method for shape classification based on optimal mass transportation theory. The Riemann surfaces are mapped onto canonical domains conformally based on uniformization theorem. The conformal factor function is treated as probability distribution on the canonical domain. For each pair of probability distributions, the optimal mass transportation map is computed by solving Monge-Amper´e equation. The transportation cost is theWasserstein distance between two distributions. By using this distance, geometric classification based on clustering can be performed. The method is applied to 3D human facial expression recognition, which demonstrates the efficiency and efficacy of the method.

Many statistical models seek relationship between variables via subspaces of reduced dimensions. For instance, in factor models, variables are roughly distributed around a low dimensional subspace determined by the loading matrix; in mixed linear regression models, the coefficient vectors for different mixtures form a subspace that captures all regression functions; in multiple index models, the effect of covariates is summarized by the effective dimension reduction space.