Research And Design On True Random Number Generator Based On Biometric Characteristics

Random number generators are widely used in various computer applications such as processes schedule, electronic games, Monte-Carlo methods on numerical analysis and statistical sampling techniques. Moreover, it is a necessary component for cryptographic algorithms and protocols. the keys for symmetric cryptosystems or the public/private key pairs for asymmetric cryptosystems should be generated randomly. RNGs are also used to create challenges, nonces, padding bytes, and blinding values in many cryptographic protocols. There are two basic types of generators used to produce random sequences: true random number generators (TRNGs) or pseudorandom number generators (PRNGs). TRNGs are usually based on physical phenomena such as thermal noise, atmospheric noise, radioactive decay and even coin-tossing. They are considered to generate random sequences with a higher security. However, it is still hard to find cheap and convenient TRNGs. In this paper, a novel type of TRNGs is proposed and researched, which is based on biometric characteristics, especially the mouse movement and handwriting signature of a specific user. Some of the conclusions of this paper are listed as follows.First of all, the new type of TRNGs based on biometric characteristics are proposed and discussed. As far as we know, such TRNGs are researched for the first time. It is cheap, convenient and universal for the personal computer (PC) platform. Furthermore, such TRNG can be easily integrated with biometric cryptosystems.Secondly, the images encryption algorithms are advised to process the mouse movements traces for the construction of TRNG and production of random numbers. To improve the processing speeds, the diffusion characteristics of image encryption algorithms are studied deeply. Several image encryption algorithms are tested for the generation of random numbers, and two new proposed algorithms perform better than others in sensitivity and randomness test.Thirdly, several TRNGs based on mouse movement and hash function are proposed and compared. Hash functions can process data faster usually than encryption algorithms and the hash value is also presented random-like properties. The discretized chaotic maps inside hash functions help a lot to eliminate the common patterns among mouse movements caused by the habit of the same user. Experiments show that the revised hash function based on chaotic tent map passes all 15 NIST statistical tests while achieve satisfactory processing speed.Fourthly, TRNGs based on human handwriting signatures are studied. In contrast to signature verification techniques, where the similarity of a person's signature is extracted, the diversity of the signatures for the same user becomes more interesting for the extraction of randomness. After a great amount of attempt with different signature features, signal processing approaches, and bit extraction methods, three TRNGs based on signature feature extraction are determined, which are demonstrated fast and effective.Fifthly, a TRNG based on handwriting signature is integrated with fuzzy vault biometric cryptosystem. The signature is processed by a hash function based on improved TD-ERCS. The theoretical analysis and experiments show that such TRNG has the highest speed than those based on image encryption algorithms or hash functions. This TRNG is also implemented on a window mobile PDA.Finally, the dissertation is concluded. Some problems as well as further work are also given.