Feature Selection on Anonymized Datasets-Reference-Cited by-同舟云学术

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Feature Selection on Anonymized Datasets

Published:2023-11-14 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
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Author:

Alishahi Mina¹,Moghtadaiee Vahideh²

Affiliation:

1. Open Universiteit,Department of Computer Science,The Netherlands

2. Cyberspace Research Institute, Shahid Beheshti University,Tehran,Iran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10361260/10360496/10361521.pdf?arnumber=10361521

Reference21 articles.

1. k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

2. L -diversity

3. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

4. Add noise to remove noise: Local differential privacy for feature selection

Cited by 1 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. BMI: Bounded Mutual Information for Efficient Privacy-Preserving Feature Selection;Lecture Notes in Computer Science;2024

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