A Novel Chaotic Image Encryption using Generalized Threshold Function

In this paper, after reviewing the main points of image encryption and threshold function, we introduce the methods of chaotic image encryption based on pseudorandom bit padding that the bits be generated by the novel generalized threshold function (segmentation and self-similarity) methods. These methods decrease periodic effect of the ergodic dynamical systems in randomness of the chaotic image encryption. The essential idea of this paper is that given threshold functions of the ergodic dynamical systems. To evaluate the security of the cipher image of this scheme, the key space analysis, the correlation of two adjacent pixels and differential attack were performed. This scheme tries to improve the problem of failure of encryption such as small key space and level of security.Comment: 7 pages, 5 figures, Published in international Journal of Computer Applications (March 2012

Cryptanalysis of a key agreement protocol based on chaotic Hash

With the rapid development of theory and application of chaos, more and more researchers are focusing on chaos based cryptosystems. Recently, Guo et al.’s [X. Guo, J. Zhang, Secure group key agreement protocol based on chaotic Hash, Information Sciences 180 (2010) 4069–4074] proposed a secure key agreement protocol based on chaotic Hash. They claimed that their scheme could withstand various attacks. Unfortunately, by giving concrete attacks, we indicate that Guo et al.’s scheme is vulnerable to the off-line password guessing attack. The analysis shows Guo et al.’s scheme is not secure for practical application

A Novel True Random Number Generator Based on Mouse Movement and a One-Dimensional Chaotic Map

We propose a novel true random number generator using mouse movement and a one-dimensional chaotic map. We utilize the x-coordinate of the mouse movement to be the length of an iteration segment of our TRNs and the y-coordinate to be the initial value of this iteration segment. And, when it iterates, we perturb the parameter with the real value produced by the TRNG itself. And we find that the TRNG we proposed conquers several flaws of some former mouse-based TRNGs. At last we take experiments and test the randomness of our algorithm with the NIST statistical test suite; results illustrate that our TRNG is suitable to produce true random numbers (TRNs) on universal personal computers (PCs)