Privacy Preserving with Association Rule Mining using Evolutionary Algorithm
Sasanko Sekhar Gantayat1, Bichitrananda Patra2, Niranjan Panda3, Manoranjan Parhi4
1Sasanko Sekhar Gantayat, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India.
2Bichitrananda Patra*, Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.
3Niranjan Panda, Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.
4Manoranjan Parhi, Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11892-11899 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9701118419/2019©BEIESP | DOI: 10.35940/ijrte.D9701.118419
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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: Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance.
Keywords: Privacy Preserving, Optimization, Vertically Separated Databases, Horizontally Partitioned Databases.
Scope of the Article: Database Theory and Application.