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 8 Issue 1

Privacy Preserving Linear Regression on Distributed Databases

Fida K. Dankar^(a),(b),(*)

Transactions on Data Privacy 8:1 (2015) 3 - 28

Abstract, PDF

(a) Sidra Medical and Research Center, Doha, Qatar.

(b) IBM, Toronto, Canada.

e-mail:fdankar @sidra.org

Abstract

Studies that combine data from multiple sources can tremendously improve the outcome of the statistical analysis. However, combining data from these various sources for analysis poses privacy risks. A number of protocols have been proposed in the literature to address the privacy concerns; however they do not fully deliver on either privacy or complexity. In this paper, we present a (theoretical) privacy preserving linear regression model for the analysis of data owned by several sources. The protocol uses a semi-trusted third party and delivers on privacy and complexity.

^* Corresponding author.