Abstract
Data Analytics is becoming an essential business tool for many data intensive companies and organizations. However, the increased use of such methods comes with the threat of data disclosure. Privacy-preserving methods have been developed with varying degrees of efficiency with the main goal of protecting individuals' privacy. This tutorial aims at presenting models and techniques of preserving privacy in machine learning and data mining.
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Published In
March 2019
67 pages
ISBN:9781450361781
DOI:10.1145/3309182
- General Chair:
- Rakesh Verma,
- Program Chairs:
- Devika Subramaniam,
- Andrew Sung,
- Rakesh Verma
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Publication History
Published: 13 March 2019
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- Research-article
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- National Science Foundation
- Department of Homeland Security
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CODASPY '19
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CODASPY '19: Ninth ACM Conference on Data and Application Security and Privacy
March 27, 2019
Texas, Richardson, USA
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