A report on Privacy Preservation Methods on Big Data, Social Network Data, Medical Data
Guna Sekhar Tirumalasetti1, Bhargava Sai Sathvik Gontla2, Vijayakumar Kuppusamy3
1Guna Sekhar Tirumalasetti, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
2Bhargava Sai Sathvik Gontla, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
3Vijayakumar Kuppusamy, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
Manuscript received on June 13, 2020. | Revised Manuscript received on June 30, 2020. | Manuscript published on July 10, 2020. | PP: 482-492 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7274079920 | DOI: 10.35940/ijitee.I7274.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a days Privacy-preserving of data is an essential philosophy in the digital computer era. As the leakage of preserved data has been increasing in the daily life. In this survey paper we have collected the different types of privateness maintenance in the view of big records of data , clinical and social networking data. In this paper we had explained deeply about the privacy preserving methods on these three types of data. We also gathered the various implementations which are implemented by various researchers until the present trend. In this survey paper we have given a clean view on the kind of arts of the secure and privateness maintaining techniques of big analytic data, in medical field data, and in social networking data. We also mentioned about the future works to be done for preserving the data. In now a days the attackers who stole the preserved data has been increased so we should increase the security levels of privacy gradually. In this survey paper we had mentioned what are the infrastructures that are preserving based on the type of data.
Keywords: Sensitivity, Touchy degree, Clustering, ppdp, Bottom-up generalization, Social network facts, Privateness assaults, Anonymized graphs, Privacy retaining, Records privacy, Access manipulate, Blockchain, encryption, Medical data, Privacy, Security.
Scope of the Article: Clustering