COMBINE DIFFERENTIAL PRIVACY AND PIR FOR EFFICIENT STRONG LOCATION PRIVACY

MPhil Thesis Defence


Title: "COMBINE DIFFERENTIAL PRIVACY AND PIR FOR EFFICIENT STRONG LOCATION 
PRIVACY"

By

Mr. King-Hong FUNG


Abstract

Data privacy is a huge concern nowadays. In the context of location based 
services, a very important issue regards protecting the position of users 
issuing queries. Strong location privacy renders the user position 
indistinguishable from any other location. This necessitates that every 
query, independently of its location, should retrieve the same amount of 
information, determined by the query with the maximum requirements. 
Consequently, the processing cost and the response time are prohibitively 
high for datasets of realistic sizes. In this thesis, we propose a novel 
solution that offers both strong location privacy and efficiency by 
adjusting the accuracy of the query results. Our framework seamlessly 
combines the concepts of -differential privacy and private information 
retrieval (PIR), exploiting query statistics to increase efficiency 
without sacrificing privacy. We experimentally show that the proposed 
approach outperforms the current state-of-the-art by orders of magnitude, 
while introducing only a small bounded error.


Date:			Tuesday, 5 May 2015

Time:			1:00pm - 3:00pm

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Dimitris Papadias (Supervisor)
 			Dr. Lei Chen (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****