1. How to submit my research paper? What’s the process of publication of my paper?
The journal receives submitted manuscripts via email only. Please submit your research paper in .doc or .pdf format to the submission email: jait@etpub.com.
2.Can I submit an abstract?
The journal publishes full research papers. So only full paper submission should be considered for possible publication. Papers with insufficient content may be rejected as well, make sure your paper is sufficient enough to be published...[Read More]

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
Copyright © 2013-2019. JAIT. All Rights Reserved
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 3.0 Unported License.