Publication Date

Spring 5-26-2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Chris Pollett

Second Advisor

Melody Moh

Third Advisor

Mahesh Subedi

Abstract

This project implements a privacy system for statistics generated by the Yioop search and discussion board system. Statistical data for such a system consists of various counts, sums, and averages that might be displayed for groups, threads, etc. When statistical data is made publicly available, there is no guarantee of preserving the privacy of an individual. Ideally, any data extracted should not reveal any sensitive information about an individual. In order to help achieve this, we implemented a Differential Privacy mechanism for Yioop. Differential privacy preserves privacy up to some controllable parameters of the number of items or individuals being aggregated when statistics from a database are made public. With this measure, reasonably accurate information about the database is provided while at the same time, privacy of the individual is maintained. The privacy mechanism called ε-differential privacy (Dwork, 2006) achieves this by adding some appropriately chosen random noise to the query’s answer in such a way that the information retrieved by the user is still accurate and at the same time no sensitive information is leaked about an individual. We implemented Differential Privacy for group’s statistics page. These pages display provide various statistics about a discussion group including its threads and wikis. We also implemented ε-differential privacy for query statistics page that displays the statistics about each query entered by a user in the Yioop’s search bar. The project also adds an additional level of privacy by using encryption of an application level database information to secure some sensitive data.

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