Multivariate equi-width data swapping for private data publication

Yidong Li; Hong Shen

doi:10.1007/978-3-642-13657-3_24

Multivariate equi-width data swapping for private data publication

Yidong Li, Hong Shen

University of Adelaide

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In many privacy preserving applications, specific variables are required to be disturbed simultaneously in order to guarantee correlations among them. Multivariate Equi-Depth Swapping (MEDS) is a natural solution in such cases, since it provides uniform privacy protection for each data tuple. However, this approach performs ineffectively not only in computational complexity (basically O(n³) for n data tuples), but in data utility for distance-based data analysis. This paper discusses the utilisation of Multivariate Equi-Width Swapping (MEWS) to enhance the utility preservation for such cases. With extensive theoretical analysis and experimental results, we show that, MEWS can achieve a similar performance in privacy preservation to that of MEDS and has only O(n) computational complexity.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages	208-215
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13657-3_24
Publication status	Published - 2010
Externally published	Yes
Event	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India Duration: 21 Jun 2010 → 24 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6118 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Country/Territory	India
City	Hyderabad
Period	21/06/10 → 24/06/10

Keywords

Data swapping
Equi-width partitioning
Multivariate data perturbation
Private data publication

Access to Document

10.1007/978-3-642-13657-3_24

Cite this

Li, Y., & Shen, H. (2010). Multivariate equi-width data swapping for private data publication. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 1 ed., pp. 208-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13657-3_24

Li, Yidong ; Shen, Hong. / Multivariate equi-width data swapping for private data publication. Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. pp. 208-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "In many privacy preserving applications, specific variables are required to be disturbed simultaneously in order to guarantee correlations among them. Multivariate Equi-Depth Swapping (MEDS) is a natural solution in such cases, since it provides uniform privacy protection for each data tuple. However, this approach performs ineffectively not only in computational complexity (basically O(n3) for n data tuples), but in data utility for distance-based data analysis. This paper discusses the utilisation of Multivariate Equi-Width Swapping (MEWS) to enhance the utility preservation for such cases. With extensive theoretical analysis and experimental results, we show that, MEWS can achieve a similar performance in privacy preservation to that of MEDS and has only O(n) computational complexity.",

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Li, Y & Shen, H 2010, Multivariate equi-width data swapping for private data publication. in Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6118 LNAI, pp. 208-215, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, India, 21/06/10. https://doi.org/10.1007/978-3-642-13657-3_24

Multivariate equi-width data swapping for private data publication. / Li, Yidong; Shen, Hong.
Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. p. 208-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Li Y, Shen H. Multivariate equi-width data swapping for private data publication. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 ed. 2010. p. 208-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13657-3_24

Multivariate equi-width data swapping for private data publication

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