Main Article Content
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.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright is retained by the authors. By submitting to this journal, the author(s) license the article under the Creative Commons License – Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), unless choosing a more lenient license (for instance, public domain). Furthermore, the authors of articles published by the journal grant the journal the right to store the articles in its databases for an unlimited period of time and to distribute and reproduce the articles electronically.
Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.
National Science Foundation
Grant numbers EIA–0131884;SES– 0345441;DMS–0112069