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.
CITATION STYLE
Ghemri, L. (2019). Preserving privacy in data analytics. In IWSPA 2019 - Proceedings of the ACM International Workshop on Security and Privacy Analytics, co-located with CODASPY 2019 (pp. 3–4). Association for Computing Machinery, Inc. https://doi.org/10.1145/3309182.3311786
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