Providing the security on the basis of encryption standards is considered as key challenges for achieving the integrity & confidentiality. There are three main public-key cryptosystem contenders. Each has a variable key size that can be increased to achieve higher security at the cost of slower cryptographic operations. The best attack known on each public-key cryptosystem requires an amount of computation determined by a security parameter which is related to the key size. The secondary factor is confidentiality i.e. ensuring that adversaries gain no intelligence from a transmitted message. There are two major techniques for achieving confidentiality:
This work proposes a novel prototype ECDSA which provides the security where there is not complete trust between documents’ sender and receiver & something more than authentication is needed. The signature is formed by taking the hash of the message and encrypting the message with the creator’s private key. It guarantees the source and integrity of the message. Then a suitable digital signature algorithm will be picked out as a result of comparing and analyzing three main digital signature algorithms in this paper. Finally, a scheme of digital signature in electronic government will be proposed in order to settle some specific problems such as spilling out the secret, forging or denial and so on. Besides, a brief analysis regarding security will be given for this scheme.

Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioural features of a person. In this paper gender recognition from human gait in image sequence have been successfully investigated. Silhouette of 15 males and 15 females from the database collected from CASIR site have been extracted. The computer vision based gender classification is then carried out on the basis of standard deviation, centre of mass and height from head to toe using Feed Forward Back Propagation Network with TRAINLM as training functions, LEARNGD as adaptation learning function and MSEREG as performance function. Experimental results demonstrate that the present gender recognition system achieve recognition performance of 93.4%, 94.6%, and 94.7% with 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons respectively. When the performance function is replaced with SSE the recognition performance is increased by 2%, 2.4% and 3% respectively for 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons.The above study indicates that Gait based gender recognition is one of the best reliable biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to quickly detect threats and provide differing levels of access to different user groups.