Seminar: Privacy-Preserving Statistical Analysis Methods and their Applications on Health Research

Ahoora Sadeghi Boroujerdi
M.Sc. Thesis Proposal
Supervisor: Dr. Saeed Samet

Privacy-Preserving Statistical Analysis Methods and their Applications on Health Research

Department of Computer Science
Thursday, January 28, 2016, 1:00 p.m., Room EN 2022


Abstract

Privacy consideration in health data usually prevents researchers and other data users from conducting their research. Also, data is distributed through various health organizations such as hospitals, thus gathering distributed health information becomes impractical. Various approaches have been proposed to preserve the patients’ privacy, whilst allowing researchers to perform mathematical operations and statistical analysis methods on health data, such as anonymization and secure computation. Data anonymization reduces the accuracy of the original data; hence the final result would not be precise enough. In addition, there are several known attacks on anonymized data, such as using public information and background knowledge to re-identify the original data. On the other hand, secure computation is more precise and the risk of data re-identification is zero; however it is computationally less efficient than data anonymization. In this thesis, we will implement a web-based secure computation framework and propose new secure statistical analysis methods. Using the proposed web application, researchers and other data users would be able to perform popular statistical analysis methods on distributed data. They will be able to perform mathematical operations and statistical analysis methods as queries through different data owners, and receive the final result without revealing any sensitive information. Digital Epidemiology Chronic Disease Tool (DEPICT) database, which contains real patients’ information, will be used to demonstrate the applicability of the web application.

Keywords:
Secure Multiparty Computation; Web-Based Framework, Privacy-Preserving; Homomorphic Encryption, Secure Statistical Analysis Methods

 

Contact

Department of Computer Science

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St. John's, NL A1B 3X9 CANADA

Tel: (709) 864-2530

Fax: (709) 864-2552

becomestudent@mun.ca