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Privacy-Preserving Logistic Regression

Saeed Samet
eHealth Research Unit, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
Abstract—Logistic regression is an important statistical analysis methods widely used in research fields, including health, business and government. On the other hand preserving data privacy is a crucial aspect in every information system. Many privacy-preserving protocols have been proposed for different statistical techniques, with various data distributions, owners and users. In this paper, we propose a new method to securely compute logistic regression of data, privately shared among two or more data owners. Using this secure protocol, data users can receive the coefficient vector of logistic regression from the data owners, who jointly execute a privacy-preserving protocol, in which only encrypted values are exchanged between them. At the end of the protocol, each data owner will send her portion of the final results to the user to construct the final query result. We have tested our method along with the secure building blocks using sample data to illustrate the performance of the results in terms of computational and communication complexities.

Index Terms—privacy-preserving, secure multiparty computation, cryptography, homomorphic encryption, statistical analysis

Cite: Saeed Samet, "Privacy-Preserving Logistic Regression," Vol. 6, No. 3, pp. 88-95, August, 2015. doi: 10.12720/jait.6.3.88-95
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