Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics
V. Shoba1, R. Parameswari2
1V. Shoba, Research Scholar, Department of Computer Science, Vels Institute of Science Technology & Advanced Studies, Chennai (Tamil Nadu), India.
2Dr. R. Parameswari, Associate Professor, Department of Computer Science, Vels Institute of Science Technology & Advanced Studies, Chennai (Tamil Nadu), India.
Manuscript received on 19 January 2020 | Revised Manuscript received on 02 February 2020 | Manuscript Published on 05 February 2020 | PP: 155-158 | Volume-8 Issue-4S5 December 2019 | Retrieval Number: D10121284S519/2019©BEIESP | DOI: 10.35940/ijrte.D1012.1284S519
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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: Big data offers various services like storing sensitive, private data and maintaining the data. Big data users may upload encrypted data rather than raw data for preserving data. Processing and analyzing the encrypted data is the primary target for attackers and hackers. Homomorphic Re-Encryption to supports access control, processed cipher-text on encrypted data and ensure data confidentiality. However, the limitation of Homomorphic Re-Encryption is the single-user system, which means it allows the party that owns a homomorphic decryption key to decrypt processed cipher-texts. Original Homomorphic Re-Encryption cannot support multiple users to access the processed cipher texts flexibly. In this paper, propose a Privacy-Preserving Big Data Processing system which support of a Homomorphic Re-Encryption using laplacian phase that extends partially from a single-group user system by offering cipher text re-encryption that allows accessing processed cipher-texts. Through the cooperation of a Data Provider, to increase the flexibility and security of our system, However apply multiple Services to take in charge of the data from their users and design computing operations over cipher-texts belonging to multiple Service. The analysis completed on proves that our Preserving the Privacy of Big Data Processing method’s to performance in terms of security is good on some datasets, inefficiency this also ensures the security and user privacy.
Keywords: Big Data, Homomorphic Re-Encryption, Paillier, Laplacian, Security, Privacy Preserving.
Scope of the Article: Big Data Analytics and Business Intelligence