Tsai Chao-Jen, Wang Huan-Chih, Wu Ja-Ling
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
Graduate Institute of Networking and Multimedia and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
Entropy (Basel). 2019 Jan 9;21(1):40. doi: 10.3390/e21010040.
In this work, three techniques for enhancing various chaos-based joint compression and encryption (JCAE) schemes are proposed. They respectively improved the execution time, compression ratio, and estimation accuracy of three different chaos-based JCAE schemes. The first uses auxiliary data structures to significantly accelerate an existing chaos-based JCAE scheme. The second solves the problem of huge multidimensional lookup table overheads by sieving out a small number of important sub-tables. The third increases the accuracy of frequency distribution estimations, used for compressing streaming data, by weighting symbols in the plaintext stream according to their positions in the stream. Finally, two modified JCAE schemes leveraging the above three techniques are obtained, one applicable to static files and the other working for streaming data. Experimental results show that the proposed schemes do run faster and generate smaller files than existing JCAE schemes, which verified the effectiveness of the three newly proposed techniques.
在这项工作中，提出了三种用于增强各种基于混沌的联合压缩与加密（JCAE）方案的技术。它们分别提高了三种不同基于混沌的JCAE方案的执行时间、压缩率和估计精度。第一种技术使用辅助数据结构显著加速现有的基于混沌的JCAE方案。第二种技术通过筛选出少量重要子表来解决巨大的多维查找表开销问题。第三种技术通过根据明文流中符号的位置对其进行加权，提高了用于压缩流数据的频率分布估计的准确性。最后，得到了两种利用上述三种技术的改进JCAE方案，一种适用于静态文件，另一种适用于流数据。实验结果表明，所提出的方案比现有的JCAE方案运行速度更快且生成的文件更小，这验证了新提出的三种技术的有效性。
