Privacy-preserving Maximum Likelihood Estimation for Distributed Data

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Xiaodong Lin
Alan F. Karr


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.

Article Details

How to Cite
Lin, Xiaodong, and Alan F. Karr. 2010. “Privacy-Preserving Maximum Likelihood Estimation for Distributed Data”. Journal of Privacy and Confidentiality 1 (2).

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