Secured Health Protective Services in Social Media Network Using K-Anonymity
N. A. S. Vinoth1, M. Yohapriyaa2, K. Janani3, V. Vijaypriya4
1N. A. S. Vinoth, Assistant Professor, Department of Software Engineering, SRMIST, Kattankulathur (Tamil Nadu), India.
2M. Yohapriyaa, Teaching Associate, Department of Software Engineering, SRMIST, Kattankulathur (Tamil Nadu), India.
3K. Janani, Teaching Associate, Department of Software Engineering, SRMIST, Kattankulathur (Tamil Nadu), India.
4V. Vijaypriya, Teaching Associate, Department of Software Engineering, SRMIST, Kattankulathur (Tamil Nadu), India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 13 August 2019 | Manuscript Published on 27 August 2019 | PP: 244-247 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10450782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1045.0782S419
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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: Healthcare media provides a capable model in order to fascinate consumer to discuss their health information and access health protection facilities from connected caretakers. Since the traditional health care service is time consuming process, we are switching to online health care practice, which can reduce the gap between care takers and patients. Due to the frankness of the social network the trust between the patients and care takers is a challenging issue and there is a chance of revealing personal information of the patients. Here, we intended to propose a reliable health protection service to facilitate user in social media networks, we deploy Bloom Filter technique for suitable personalized caretakers, in order to assure trustworthy rankings and critiques for caretakers, we include Sybil attack detection technique to identify users’ fake rankings and critiques using various false name. It incorporates generalization and suppression techniques to protect individual’s private data. For this purpose, k-anonymity Technique is implemented to anonymize the data.
Keywords: Sybil Attack, Bloom Filter, K-Anonymity, Linkage Attack, Local Recoding.
Scope of the Article: Social Networks