Articles in Press

Accepted articles here

Latest Issues

Volume 18 Issue 2
Volume 18 Issue 1

Volume 17 Issue 3
Volume 17 Issue 2
Volume 17 Issue 1

Volume 16 Issue 3
Volume 16 Issue 2
Volume 16 Issue 1

Volume 15 Issue 3
Volume 15 Issue 2
Volume 15 Issue 1

Volume 14 Issue 3
Volume 14 Issue 2
Volume 14 Issue 1

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1

Volume 17 Issue 2

RWN: A Novel Neighborhood-Based Method for Statistical Disclosure Control

Noah Perry^(a), Norman Matloff^(b),(*), Patrick Tendick^(c)

Transactions on Data Privacy 17:2 (2024) 55 - 88

Abstract, PDF

(a) Department of Statistics, University of California, Davis.

(b) Department of Computer Science, University of California, Davis.

(c) TEKsystems, Inc.

e-mail:; nsmatloff @ucdavis.edu;

Abstract

A novel variation of the data swapping approach to statistical disclosure control is presented, aimed particularly at preservation of multivariate relations in the original dataset for proper statistical analysis. A theorem is proved in support of the method, and extensive empirical investigation is reported.

^* Corresponding author.