Biblio

The robustness of the encryption systems in all of their types depends on the key generation. Thus, an encryption system can be said robust if the generated key(s) are very complex and random which prevent attackers or other analytical tools to break the encryption system. This paper proposed an enhanced key generation based on iris image as biometric, to be implemented dynamically in both of authentication process and data encryption. The captured iris image during the authentication process will be stored in a cloud server to be used in the next login to decrypt data. While in the current login, the previously stored iris image in the cloud server would be used to decrypt data in the current session. The results showed that the generated key meets the required randomness for several NIST tests that is reasonable for one use. The strength of the proposed approach produced unrepeated keys for encryption and each key will be used once. The weakness of the produced key may be enhanced to become more random.

Non-interference is a program property that ensures the absence of information leaks. In the context of programming languages, there exist two common approaches for establishing non-interference: type systems and program logics. Type systems provide strong automation (by means of type checking), but they are inherently restrictive in the kind of programs they support. Program logics support challenging programs, but they typically require significant human assistance, and cannot handle modules or higher-order programs.To connect these two approaches, we present SeLoC—a separation logic for non-interference, on top of which we build a type system using the technique of logical relations. By building a type system on top of separation logic, we can compositionally verify programs that consist of typed and untyped parts. The former parts are verified through type checking, while the latter parts are verified through manual proof.The core technical contribution of SeLoC is a relational form of weakest preconditions that can track information flow using separation logic resources. SeLoC is fully machine-checked, and built on top of the Iris framework for concurrent separation logic in Coq. The integration with Iris provides seamless support for fine-grained concurrency, which was beyond the reach of prior type systems and program logics for non-interference.

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of The National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data.

Biometrics has enormous role to authenticate or substantiate an individual's on the basis of their physiological or behavioral attributes for pattern recognition system. Multimodal biometric systems cover up the limitations of single/ uni-biometric system. In this work, the multimodal biometric system is proposed; iris and hand geometry features are fused at feature level. The iris features are extracted by using moments and morphological operations are used to extract the features of hand geometry. The Chaos-based encryption is applied in order to enhance the high security on the database. Accuracy is predicted by performing the matching process. The experimental result shows that the overall performance of multimodal system has increased with accuracy, Genuine Acceptance Rate (GAR) and reduces with False Acceptance Rate (FAR) and False Rejection Rate (FRR) by using chaos with iris and hand geometry biometrics.

BP algorithm used by autoencoder classification algorithm. But the BP algorithm is not only complicated and inefficient, but sometimes falls into local optimum. This makes autoencoder classification algorithm are not very good. So in this paper we combie Quantum Particle Swarm Optimization (QPSO) and autoencoder classification algorithm. QPSO used to optimize the weight of autoencoder neural network and the parameter of softmax. This method has been tested on some database, and the experimental result shows that this method has got good results.

Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data.

The new era of information communication and technology (ICT), everyone wants to store/share their Data or information in online media, like in cloud database, mobile database, grid database, drives etc. When the data is stored in online media the main problem is arises related to data is privacy because different types of hacker, attacker or crackers wants to disclose their private information as publically. Security is a continuous process of protecting the data or information from attacks. For securing that information from those kinds of unauthorized people we proposed and implement of one the technique based on the data modification concept with taking the iris database on weka tool. And this paper provides the high privacy in distributed clustered database environments.