Small Domain Randomization: Same Privacy, More Utility

Summary: Introduces small domain randomization to improve utility in privacy-preserving data publishing. Partitions data into sub-tables with smaller sensitive-value domains, enabling higher retention probabilities and independent perturbation while preserving privacy bounds. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10109
Venue: VLDB
Year: 2010
Pagerank: 4.3556432e-05
Overall Rank: 9,336 | 35.06%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Rhonda Chaytor
2. Ke Wang

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
12,130	Publishing Microdata with a Robust Privacy Guarantee	2012	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074232718
83	Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis	2009	SIGMOD	0.00053933811
177	Limiting Privacy Breaches in Privacy Preserving Data Mining	2003	PODS	0.0003788711
654	Anatomy: Simple and Effective Privacy Preservation	2006	VLDB	0.00018613167
955	Privacy Preserving OLAP	2005	SIGMOD	0.00015075131
1,761	The Boundary Between Privacy and Utility in Data Publishing	2007	VLDB	0.00010651764
4,509	Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge	2007	VLDB	6.1270304e-05
4,794	Optimal Random Perturbation at Multiple Privacy Levels	2009	VLDB	5.9161511e-05
4,979	Fast Data Anonymization with Low Information Loss	2007	VLDB	5.7878768e-05

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