Privacy Preserving Data Analysis using Decision Tree Learning Algorithm Through Additive Homomorphic Encryption
K. Durga Prasad1, D. Vasumathi2
1K.Durga Prasad, JNTUK Research Scholar, JNT University, Kakinada, (A.P), India.
2D.Vasumathi, Professor, Department of Computer Science Engineering, JNTUHCEH, JNT University, Kukatpally, Hyderabad (Telangana), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 267-272 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2738028419/19©BEIESP
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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 is an emerging concern in the field of data mining. The Randomization technique protects privacy with loss of accuracy. The secure multi-party computation increases the accuracy and conserves privacy but the computational complexity is more. The encryption of data using cryptography makes the data secure without loss of accuracy and reduces the communication complexity. The proposed technique is privacy preserving decision tree algorithm using cryptographic approach. The data miner collects frequencies and combined frequencies from the users and learns the classification rules from the decision tree. The data miner learns only frequencies of the sensitive data. The experimental result shows that proposed privacy preserving decision tree algorithm is computationally efficient and the accuracy is more than the randomization models. The communication complexity is less compared with the secure multi-party computation models.
Keyword: Cryptographic Encryption, Data Analysis, Decision Tree and Privacy Preserving.
Scope of the Article: Predictive Analysis