資料流基於混沌之同時壓縮與加密方法

Abstract

本文提出一種全新的基於混沌之同時壓縮與加密的方法，並首次提出可使用在資料流(streaming data)上基於混沌之同時壓縮與加密的架構。現有基於混沌的同時壓縮與加密方法都是先建好完整的搜尋表格再進行查找編碼，本文提出的新方法則是在編碼的過程中動態的使表格成長。此方法解決了使用多維表格時，表格過大而影響壓縮效能的問題，使得不論輸入檔案的大小都能擁有良好的壓縮效果。基於動態建表的因素，此方法特別適用在具有局部特徵(local feature)的檔案，實驗證實在標準資料庫中的各種尺寸檔案都擁有不亞於甚至優於過往方法的壓縮率及執行時間，而在此同時資料安全性也得到了一定的保證。

An approach for improving the compression performance of chaos-based joint compression and encryption schemes is proposed. Instead of building the whole lookup tables before encryption, we dynamically develop the bi-gram lookup tables during the encryption steps. This dynamically construction model can solve the problem of huge overheads of LUTs and further enhance the performances. Moreover, unlike the previous related works, which are all static, we also propose a streaming scheme in addition to a static approach. Simulation results show that that the proposed modification leads to a better compression performance while the security is not compromised.