The Generic Framework of Privacy Preserving Data Mining Phases: Challenges & Future Directions-Reference-Cited by-同舟云学术

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The Generic Framework of Privacy Preserving Data Mining Phases: Challenges & Future Directions

Published:2021-12-05 Issue: Volume: Page:
ISSN:
Container-title:2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)
language:
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Author:

Abdelhameed Saad A.¹,Moussa Sherin M.²,Badr Nagwa L.²,Essam Khalifa M.³

Affiliation:

1. Institute of National Planning,Planning Techniques Center,Cairo,Egypt

2. Ain Shams University,Faculty of Computer and Information Sciences,Information Systems Department,Cairo,Egypt

3. Egyptian Chinese University,Faculty of Engineering and Technology,SW Engineering & Information Technology Department,Cairo,Egypt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9693993/9694107/09694174.pdf?arnumber=9694174

Reference55 articles.

1. A fully homomorphic encryption scheme;gentry,2009

2. Secure Multiparty Computation for Privacy Preserving Data Mining

3. Random projection-based multiplicative data perturbation for privacy preserving distributed data mining;liu;IEEE Transactions on Knowledge and Data Engineering,2005

4. Outlier Detection

5. A random rotation perturbation approach to privacy preserving data classification;chen,2005

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CHAINS: CHAIN-Based Fusion Safety System Framework for Intelligent Connected Vehicle;CHAIN;2024-03

2. Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation;Computer Systems Science and Engineering;2023

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