20 20

Transactions on
Data Privacy
Foundations and Technologies


Articles in Press

Accepted articles here

Latest Issues

Year 2016

Volume 9 Issue 2
Volume 9 Issue 1

Year 2015

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

Year 2014

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

Year 2013

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

Year 2012

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

Year 2011

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

Year 2010

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

Year 2009

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

Year 2008

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

Volume 4 Issue 1

Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data

Rathindra Sarathy(a), Krishnamurty Muralidhar(b),(*)

Transactions on Data Privacy 4:1 (2011) 1 - 17

Abstract, PDF

(a) Spears School of Business; Oklahoma State University; Stillwater OK 74078; USA.

(b) Gatton College of Business and Economics; University of Kentucky; Lexington KY 40506; USA.

e-mail:rathin.sarathy @okstate.edu; krishm @uky.edu


Laplace noise addition is often advanced as an approach for satisfying differential privacy. There have been several illustrations of the application of Laplace noise addition for count data, but no evaluation of its performance for numeric data. In this study we evaluate the privacy and utility performance of Laplace noise addition for numeric data. Our results indicate that Laplace noise addition delivers the promised level of privacy only by adding a large quantity of noise for even relatively large subsets. Because of this, even for simple mean queries, the responses for a masking mechanism that uses Laplace noise addition is of little value. We also show that Laplace noise addition may be vulnerable to a tracker attack. In order to avoid this, it may be necessary to increase the variance of the noise added as a function of the number of queries issued. This implies that the utility of the responses would be further reduced. These results raise serious questions regarding the viability of Laplace based noise addition for masking numeric data.

* Corresponding author.

Follow us



ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; U. of Skövde; PO Box 408; 54128 Skövde; (Sweden); e-mail:tdp@tdp.cat


Vicenç Torra, Last modified: 10 : 38 June 27 2015.