Achieving High Data Utility K-Anonymization Using Similarity-Based Clustering Model-Reference-Cited by-同舟云学术

Achieving High Data Utility K-Anonymization Using Similarity-Based Clustering Model

Published:2016 Issue:8 Volume:E99.D Page:2069-2078
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

SARRAFI AGHDAM Mohammad Rasool¹,SONEHARA Noboru¹²

Affiliation:

1. Graduate University for Advanced Studies (SOKENDAI), School of Multidisciplinary, Informatics Department

2. National Institute of Informatics (NII)

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E99.D/8/E99.D_2015INP0019/_pdf

Reference23 articles.

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2. [2] P. Golle, “Revisiting the Uniqueness of Simple demographics in the US Population,” Workshop on Privacy in the Electronic Society (WPES), pp.77-80, Alexandria, Virginia, USA, Oct. 30, 2006.

3. [3] P. Samarati and L. Sweeney, “Generalizing data to provide anonymity when disclosing information,” Proc. ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (PODS '98), ACM, p.188, 1998.

4. [4] P. Samarati, “Protecting Respondents' Identities in Microdata Release,” IEEE Trans. Knowl. Data Eng., vol.13, no.6, pp.1010-1027, Nov./Dec. 2001.

5. [5] G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, “Approximation algorithms for k-anonymity,” Journal of Privacy Technology, pp.1-18, 2005.

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