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.
|Number of pages||26|
|Journal||Transactions on Data Privacy|
|Publication status||Published - Jan 1 2015|
ASJC Scopus subject areas
- Statistics and Probability