Papers by Keyword: Privacy Preservation

Abstract: It is important to ensure the integrity of the aggregation result and quality of communication among node in wireless sensor network in the condition of high-efficient privacy-preserving data aggregation mechanism. In this paper, we propose a method based on reputation model to solve this problem. During the process of data aggregation, the cluster head node will choose the node with max reputation value as collaborative node to accomplish the aggregation process. To show the efficacy and efficiency of this method, we present the simulation results. The simulation results show that our method iE-CPDA(enforced Integrity Energy-efficient Cluster-based Privacy-preserving Data Aggregation) has less communication and computation and higher accuracy than iCPDA.

Abstract: The Internet of Things is a novel paradigm in the scenario of modern wireless telecommunications. It has high impact on several aspects of everyday-life and behavior of potential users. However, it raises serious concerns over individual privacy in the new environment of smart things. In this paper, we provide for an overview on privacy preserving techniques in the Internet of Things and discuss the present problems and directions for future research.

Abstract: This paper analyzes the traditional outsourcing model (TOM). Aiming at that TOM has disadvantages such as low security on User Privacy, this paper proposes a new access model in outsourced database. Using the trusted third party (TTP ) makes all operations in the database become anonymous in order to achieve the purpose of user privacy protection.

Abstract: The existing Extensible Authentication Protocol (EAP) based handover authentication schemes have show robust security features especially the Qi Jing et al.'s design, which not only meets the essential security requirements in handover authentication but also achieves privacy preservation. However, it still suffers pitfalls in the process of authentication. The main idea of this paper is to extend the work by Qi Jing et al. and particularly focus on the formal analysis using extending BAN logic which is more concise yet practical to use on PKI-based protocols.

Abstract: In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.

Abstract: In Open Computing Environments, more and more applications are deployed as service. They provide services for users from different organizations. Resources are deployed different organizations or enterprises. Users could access them across organizational boundaries. To ensure the security of resources, users have to provide their identity information to be authorized correctly every time they access a new service. It raises the risk of privacy which identity information was transferred frequently. In this paper, we demonstrate the problem of privacy in Identity Management of open computing environments, discuss the risk of privacy.