Abstract
Due to the wide deployment of Internet and information technology for storage and processing of data, the ever-growing privacy concern is the major obstacle for information sharing. In the present digital scenario, the information security is of prime concern. With hundreds of terabytes or even Petabytes of data/information floating over around, it is important to have the access to the private sensitive data only to authorized users. The explosive increase in the amount of data/information leads to the growth of data mining techniques, a significant resource for information security. The data mining is the extrication of relevant patterns/ knowledge of information from bulk of data. It provides the variety of applicable techniques, in accordance with the different security issues aroused, to achieve a desired level of privacy. This paper provides a wide survey of the emerging issues in the security field and various privacy-preserving techniques PPDM techniques that can be used to mitigate the increasing security risks and threats. It also centers on analyzing the problem of computation on private information developing new concepts and techniques to deal with emerging privacy issues in various contexts security of information while sharing and exchange using Differential Privacy. Finally presents the challenges and techniques for differential privacy as a trusted path to achieve privacy and discuss some of the theoretical and practical challenges for future work in this area.
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Narwaria, M., Mishra, S. (2018). Semantic Security for Sharing Computing Knowledge/Information. In: Bokhari, M., Agrawal, N., Saini, D. (eds) Cyber Security. Advances in Intelligent Systems and Computing, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-8536-9_45
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DOI: https://doi.org/10.1007/978-981-10-8536-9_45
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