Hybrid and Decentralized Privacy Preservation using D-Anonymity and T-Closeness in Social Network
Annapurna Kattimani1, Vijaylakshmi M.2, Channappa B. Akki3
1Annapurna Kattimani, Assistant Professor, Department of Information Science & Engineering, ISE KLEIT, Hubballi (Karnataka), India.
2Vjayalakshmi M., Associate Professor, School of Computer Science School of Computer Science and Engineering, KLE Technological University, Vidyanagar, Hubballi (Karnataka), India.
3Dr. Channappa B. Akki, Professor, Department of Computer Science & Engineering, IIIT, (Karnataka), India.
Manuscript received on 08 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 31 December 2019 | PP: 671-675 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11091292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1109.1292S19
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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: Although Social Network (SN) knowledge is significant assets for data examination, freeing the data to the general public could reason an invasion of privacy. Privacy insurance is taken a lot of seriously than various data mining duties. The privacy problems are dealt with by several algorithms and strategies in the literature. But, perpetually there exists a trade-off between privacy and data.Our objective in this work is to design and develop a privacy-preserving solution for the social network. We have used K-anonymity and T-closeness algorithm and data anonymization. Further, data anonymization is decentralized by giving control of anonymization to the data owner. The solution is implemented on a dummy social network for testing the effectiveness of the privacy preservation solution proposed by us.
Keywords: SN (Social-Networking), K-anonymity, T-closeness, Quasi-identifier.
Scope of the Article: Social Networks