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Boyue Fang

Publications and source records attributed to Boyue Fang.

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Compact and Efficient KEMs over NTRU Lattices

The NTRU lattice is a promising candidate to construct practical cryptosystems, in particular key encapsulation mechanism (KEM), resistant to quantum computing attacks. Nevertheless, there are still some inherent obstacles to NTRU-based KEM schemes in having integrated performance, taking security, bandwidth, error probability, and computational efficiency \emph{as a whole}, that is as good as and even better than their \{R,M\}LWE-based counterparts. In this work, we solve this problem by presenting a new family of NTRU-based KEM schemes, referred to as CTRU and CNTR. By bridging low-dimensional lattice codes and high-dimensional NTRU-lattice-based cryptography with careful design and analysis, to the best of our knowledge CTRU and CNTR are the first NTRU-based KEM schemes with scalable ciphertext compression via only one \emph{single} ciphertext polynomial, and are the first that could outperform \{R,M\}LWE-based KEM schemes in integrated performance. For instance, compared to Kyber that is currently the only standardized KEM by NIST, on the recommended parameter set CNTR-768 has about $12\%$ smaller ciphertext size while encapsulating 384-bit keys compared to the fixed 256-bit key size of Kyber, security strengthened by $(8,7)$ bits for classical and quantum security respectively, and significantly lower error probability ($2^{-230}$ of CNTR-768 vs. $2^{-164}$ of Kyber-768). In comparison with the state-of-the-art AVX2 implementation of Kyber-768, CNTR-768 is faster by 1.9X in KeyGen, 2.6X in Encaps, and 1.2X in Decaps, respectively. When compared to the NIST Round 3 finalist NTRU-HRSS, our CNTR-768 has about $15\%$ smaller ciphertext size, and the security is strengthened by $(55,49)$ bits for classical and quantum security respectively. As for the AVX2 implementation, CNTR-768 is faster than NTRU-HRSS by 19X in KeyGen, 2.3X in Encaps, and 1.6X in Decaps, respectively.

cs.CR

Drug dissemination strategy with an SEIR-based SUC model

According to the features of drug addiction, this paper constructs an SEIR-based SUC model to describe and predict the spread of drug addiction. Predictions are that the number of drug addictions will continue to fluctuate with reduced amplitude and eventually stabilize. To seek the fountainhead of heroin, we identified the most likely origins of drugs in Philadelphia, PA, Cuyahoga and Hamilton, OH, Jefferson, KY, Kanawha, WV, and Bedford, VA. Based on the facts, advised concentration includes the spread of Oxycodone, Hydrocodone, Heroin, and Buprenorphine. In other words, drug transmission in the two states of Ohio and Pennsylvania require awareness. According to the propagation curve predicted by our model, the transfer of KY state is still in its early stage, while that of VA, WV is in the middle point, and OH, PA in its latter ones. As a result of this, the number of drug addictions in KY, OH, and VA is projected to increase in three years. For methodology, with the Principal component analysis technique, 22 variables in socio-economic data related to the continuous use of Opioid drugs was filtered, where the 'Relationship' Part deserves a highlight. Based on them, by using the K-means algorithm, 464 counties were categorized into three baskets. To combat the opioid crisis, a specific action will discuss in the sensitivity analysis section. After modeling and analytics, innovation is required to control addicts and advocate anti-drug news campaigns. This part also verified the effectiveness of model when $d_1<0.2; r_1,r_2,r_3<0.3; 15<β_1,β_2,β_3<25$. In other words, if such boundary exceeded, the number of drug addictions may rocket and peak in a short period.

q-bio.PE

Design of High-Frequency Trading Algorithm Based on Machine Learning

Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.

q-fin.TR