Notice of Violation of IEEE Publication Principles: Information security in big data mining

Notice of Violation of IEEE Publication Principles

"Information Security in Big Data Mining"
by T. Revathi and V. Sudharsana
in the Proceedings of the IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017, pp.2045-2049

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

"Information Security in Big Data: Privacy and Data Mining"
by Lie Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, and Yong Ren
in IEEE Access, October 2014, 1149-1176


The growing popularity and development of data mining technologies bring serious threat to the security of individual's sensitive information. An emerging research topic in datamining, known as Privacy Preserving Data Mining (PPDM), has been broadly contemplated as of late. The essential thought of PPDM is to adjust the information in such a route to perform data mining calculations viably without bargaining the security of delicate data contained in the information. Current investigations of PPDM for the most part concentrate on the best way to lessen the security hazard brought by data mining operations, while truth be told, undesirable divulgence of sensitive data may likewise occur during the time spent data gathering, data distributing, and data delivering. In this paper, we view the privacy issues related to datamining from a wider view point and examine different methodologies that can ensure delicate data. Specifically, we recognize four unique sorts of clients required in data mining applications, to be specific, data provider, data collector, data miner, and decision maker. For each kind of client, we examine his security concerns and the strategies that can be embraced to ensure sensitive data. We briefly present the nuts and bolts of related research topics, review state-of-the art approaches, and present Some preliminary thoughts on future research bearings. Other than investigating the protection safeguarding approaches for each sort of client, we additionally survey the amusement hypothetical methodologies, which are proposed for breaking down the associations among various clients in a datamining scenario, each of whom has his own valuation on the sensitive information. By differentiating the obligations of various clients regarding security of delicate data, we might want to give some valuable bits of knowledge into the investigation of PPDM.