A Comprehensive Assessment of Privacy Preserving Data Mining Techniques-Reference-Cited by-同舟云学术

A Comprehensive Assessment of Privacy Preserving Data Mining Techniques

Published:2022 Issue: Volume: Page:833-842
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
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Author:

Prasanthi Kundeti Naga,Chandra Sekhara Rao M. V. P.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7657-4_67

Reference26 articles.

1. Sweeney L (2002) K-anonymity: a model for protecting privacy. Proc Int J Uncertain Fuzziness Knowl-Based Syst

2. Vani B, Jayanthi D (2013) Efficient approach for privacy preserving microdata publishing using slicing. IJRCTT

3. Verykios VS, Bertino E, Fovino IN, Provonza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. SIGMOD Rec 33(1):50–57

4. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M (2006) I-diversity: privacy beyond k-anonymity. Proc Int Con Data Eng (ICDE):24

5. Jahan T, Narsimha G, Guru Rao CV (2012) Data perturbation and features selection in preserving privacy. In: Proceedings of 978-1-4673-1989-8/12. IEEE

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