Improving the adaptability of differential privacy

Differential privacy is a mathematical technique that provides strong theoretical privacy guarantees by ensuring statistical indistinguishability of individuals in a dataset. It has become the de facto framework for providing privacy-preserving data analysis over statistical datasets. Differential privacy has garnered significant attention from researchers and privacy experts due to its strong privacy guarantees. However, the lack of flexibility due to the dearth of configurable parameters in existing mechanisms, the accuracy loss caused by the noise added, and problems with choosing a suitable value of the privacy parameter, E, have prevented its widespread adoption in the industry. In this thesis, I address these issues. In differential privacy, the standard approach is to add Laplacian noise to the output of queries. I propose new probability distributions and noise adding mechanisms that preserve ([epsilon])-differential privacy and ([epsilon], [delta])-differential privacy.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Mugunthan, Vaikkunth.

Advisor dc:contributor.advisor

• Lalana Kagal.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122763

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122763