Biblio

Most of the existing calculation method of expert evaluation ability directly call data onto calculation, which leads to the risk of privacy leakage of expert review information and affects the peer review environment. With regard to this problem, a privacy protection method of experts' evaluation ability calculation of peer review is proposed. Privacy protection and data usability are adjusted according to privacy preferences. Using Gauss distribution and combining with the distributive law of real evaluation data, the virtual projects are generated, and the project data are anonymized according to the virtual projects. Laplace distribution is used to add noise to the evaluation sub score for perturbation, and the evaluation data are obfuscation according to the perturbation sub score. Based on the protected project data and evaluation data, the expert evaluation ability is calculated, and the review privacy is protected. The experimental results show that the proposed method can effectively balance the privacy protection and the accuracy of the calculation results.

With rapid development of deep integration of computation, control, and communication, Cyber-Physical Systems (CPSs) play an important role in industrial processes. Combined with the technology of fog computing, CPSs can outsource their complicated computation to the fog layer, which in turn, may bring security threats with regard to data privacy. To protect data privacy in a control framework, this paper investigate observer-based secure control problem towards fog computing aided CPSs (FCA-CPSs) by utilizing data perturbation method. Firstly, security inputs are designed to encrypt the transmitted states to realize specific confidentiality level. Then, sufficient conditions are established to ensure the stability of considered FCA-CPSs. Finally, a numerical example is provided to illustrate the effectiveness of the secure estimation scheme.

In Data mining is the method of extracting the knowledge from huge amount of data and interesting patterns. With the rapid increase of data storage, cloud and service-based computing, the risk of misuse of data has become a major concern. Protecting sensitive information present in the data is crucial and critical. Data perturbation plays an important role in privacy preserving data mining. The major challenge of privacy preserving is to concentrate on factors to achieve privacy guarantee and data utility. We propose a data perturbation method that perturbs the data using fuzzy logic and random rotation. It also describes aspects of comparable level of quality over perturbed data and original data. The comparisons are illustrated on different multivariate datasets. Experimental study has proved the model is better in achieving privacy guarantee of data, as well as data utility.