An algorithm for k-anonymous microaggregation and clustering inspired by the design of distortion-optimized quantizers-Reference-Cited by-同舟云学术

An algorithm for k-anonymous microaggregation and clustering inspired by the design of distortion-optimized quantizers

Published:2011-10 Issue:10 Volume:70 Page:892-921
ISSN:0169-023X
Container-title:Data & Knowledge Engineering
language:en
Short-container-title:Data & Knowledge Engineering

Author:

Rebollo-Monedero David,Forné Jordi,Soriano Miguel

Funder

Spanish Government

Government of Catalonia

Publisher

Elsevier BV

Subject

Information Systems and Management

Reference87 articles.

1. Private location-based information retrieval via k-anonymous clustering;Rebollo-Monedero,2009

2. Least squares quantization in PCM;Lloyd;IEEE Trans. Inform. Theory,1982

3. Quantizing for minimum distortion;Max;IEEE Trans. Inform. Theory,1960

4. An algorithm for least-squares estimation of nonlinear parameters;Marquardt;SIAM J. Appl. Math. (SIAP),1963

5. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression;Samarati;SRI Int., Tech. Rep.,1998

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