Preserving privacy in data analytics

0Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free