Privacy-Preserving Data Analysis without Trusted Third Party
Author:
Affiliation:
1. Osaka University,Graduate School of Engineering,Japan
2. Carnegie Mellon University,USA
3. Advanced Telecommunications Research Institute Internationa,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10063338/10063342/10063481.pdf?arnumber=10063481
Reference18 articles.
1. Scikit-learn: Machine learning in Python;pedregosa;Journal of Machine Learning Research,2011
2. Collecting and Analyzing Multidimensional Data with Local Differential Privacy
3. Extremal mechanisms for local differential privacy;kairouz;Advances in neural information processing systems,0
4. Local privacy and statistical minimax rates;duchi;54th Annual Symposium on Foundations of Computer Science,2013
5. Optimal Differentially Private Mechanisms for Randomised Response
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1. Re-visited Privacy-Preserving Machine Learning;2023 20th Annual International Conference on Privacy, Security and Trust (PST);2023-08-21
2. Balanced Privacy Budget Allocation for Privacy-Preserving Machine Learning;Lecture Notes in Computer Science;2023
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