An Efficient Distributive Framework for Preserving Data Privacy through Block Chain
D Swapna1, A Madhuri2, T Sri Lakshmi3, S Phani Praveen4
1D Swapna, Assistant Professor, Department of Computer Science Engineering, PVPSIT, Vijayawada, AP, India
2A Madhuri, Assistant Professor, Department of Computer Science Engineering, PVPSIT, Vijayawada, AP, India.
3T Sri Lakshmi, Assistant Professor, Department of Computer Science Engineering, PVPSIT, Vijayawada, AP, India.
4S Phani Praveen, Assistant Professor, Department of Computer Science Engineering, PVPSIT, Vijayawada, AP, India.
Manuscript received on 01 March 2019 | Revised Manuscript received on 05 March 2019 | Manuscript published on 30 July 2019 | PP: 5236-5239 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1052078219/19©BEIESP | DOI: 10.35940/ijrte.B1052078219
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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: Deep Learning Models has gained much attention to perform various artificial intelligence tasks. The accuracy of the models relies on the availability of data. Privacy and auditability has become the major concern for data providers. First issue is the centralised server which may become malicious causing break in privacy. Second is no incentives are given for data providers and trainers. Block chain is the most emerging innovation as of late. Decentralised connectivity of block chains gives another approach to interface information without the overheads of security, trust and controls. To address the above issues we propose an algorithm where clients send the model to the block chain for training where the honest trainers are incentivized for training, sharing weights. The weights are averaged; parameters are updated by a smart contract that resides on block chain which guarantees privacy and audit ability
Keywords: Block Chain, Network, Privacy
Scope of the Article: Block Chain-Enabled IoT Device and Data Security and Privacy