Recent technological advances enable the collection of huge amounts of data. Commonly, these data are generated, stored, and owned by multiple entities that are unwilling to cede control of their data. This distributed environment requires statistical tools that can produce correct results while preserving data privacy. Privacy-preserving protocols have been proposed to solve specific statistical analysis such as linear regression, clustering, and classification. In this paper, we present methods and protocols for privacy-preserving maximum likelihood estimation in general settings. We discuss both horizontally and vertically partitioned data, and propose procedures that allow participating parties to withdraw from the joint computation. Logistic regression is used to demonstrate our method.
Lin, Xiaodong and Karr, Alan F.
"Privacy-preserving Maximum Likelihood Estimation for Distributed Data,"
Journal of Privacy and Confidentiality: Vol. 1
, Article 6.
Available at: http://repository.cmu.edu/jpc/vol1/iss2/6