In this paper, we present an efficient attack on the multivariate Quadratic Quasigroups (MQQ) public key cryptosystem. Our cryptanalysis breaks the MQQ cryptosystem by solving a system of multivariate quadratic polynomial equations using both the MutantXL algorithm and the F4 algorithm. We present the experimental results that show that MQQ systems is broken up to size n equal to 300. Based on these results we show also that MutantXL solves MQQ systems with much less memory than the F4 algorithm implemented in Magma.

We propose a new efficient hardware implementation of Rainbow signature scheme. We enhance the implementation in three directions. First, we develop a new parallel hardware design for the Gauss-Jordan elimination, and solve a 12 ×12 system of linear equations with only 12 clock cycles. Second, a novel multiplier is designed to speed up multiplication of three elements over a finite field. Third, we design a novel partial multiplicative inverter to speed up the multiplicative inversion of finite field elements. Through further other minor optimizations of the parallelization process and by integrating the major optimizations above, we build a new hardware implementation, which takes only 198 clock cycles to generate a Rainbow signature, a new record in generating digital signatures and four times faster than the 804-clock-cycle Balasubramanian-Bogdanov-Carter-Ding-Rupp design with similar parameters.

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We discuss a general parametrization for vertices of quantum graphs and show, in particular, how the 's and 'couplings at an n-edge vertex can be approximated by means of n+ 1 couplings of the type provided the latter are properly scaled.

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more information were more similar to each other.), and developed an active learning mechanism to conduct the harmonization. Toward a total of 9,321 variables from 49 databases (i.e., 783 stemmed variables, from 1970 to 2018), we applied our model iteratively together with human reviews for four rounds, then obtained 323 hyperchains of variables. During the harmonization, the first round of our model achieved recall and precision of 87.56%, 57.70%, respectively. Our harmonized graph neural network (HGNN) method provides a feasible and efficient way to connect relevant databases at a meta-level. Adapting to the database's property and characteristics, HGNN can learn patterns globally, which is powerful to discover the similarity between variables among databases. Our proposed method provides an effective way to reduce the manual effort in database harmonization and integration of fragmented data into useful databases for future research.