Privacy-preserving Maximum Likelihood Estimation for Distributed Data
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Abstract
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.
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). https://doi.org/10.29012/jpc.v1i2.574.
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Funding data
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National Science Foundation
Grant numbers EIA–0131884;SES– 0345441;DMS–0112069