Author
Hadley, Connor
Other Contributors
Zikas, Vassilis; Magdon-Ismail, Malik; Milanova, Ana;
Date Issued
2018-05
Subject
Computer science
Degree
MS;
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
Abstract
This paper examines the problem of learning information on multiple private databases in a quick and secure manner. The owners of such databases may either not desire to or are otherwise unable to leak information about their respective databases, despite the desired information possibly being entirely benign in nature. For example, to reference an earlier paper creating the DJoin system, one may wish to study the correlation between disease and travel to certain regions of the world, but not have access to the information required for this process. This project builds upon past attempts at providing a system to answer this problem, with a focus on improving the scalability as the number of datasets being examined increases.; As such, this paper introduces the Secure Multiparty Differentially Private Mechanism, or MDP, designed to improve upon past works in this field, and offer a universal solution to the problem that works regardless of the number of parties involved.;
Description
May 2018; School of Science
Department
Dept. of Computer Science;
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection;
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;