Enhancing the Practicality of Differential Privacy

PhD Thesis Proposal Defence


Title: "Enhancing the Practicality of Differential Privacy"

by

Mr. Dajun SUN


Abstract:

Differential privacy (DP) has emerged as the de facto standard for releasing 
query results over private data. A considerable number of DP mechanisms have 
been developed to address various fundamental challenges, including mean and 
median estimation, releasing graph statistics, and answering SQL queries. 
These works aim to provide results with high utility, i.e. high accuracy, 
while ensuring DP protection. Some of these approaches have achieved 
near-optimal performance, meaning their errors closely approach the 
established DP lower bounds. Despite the advancements of current DP 
mechanisms in terms of utility, the practical adoption of DP remains 
relatively limited. This thesis seeks to enhance the practicality of DP to 
better align with real-world needs.

Firstly, we noticed that current DP mechanisms only provide a noisy query 
answer without indicating the potential error induced by this noisy result, 
which constrains further meaningful analysis. To fix this issue, we propose a 
series of differentially private confidence interval (CI) techniques that are 
(1) differentially private; (2) correct, i.e., the interval contains the true 
query answer with the specified confidence level; and (3) have good utility 
guarantee. Our techniques are applicable across a diverse range of problems, 
from basic statistical analyses to complex conjunctive queries.

Secondly, the standard DP model provides uniform privacy protection for all 
users, which may not be desirable in real applications since users may have 
different privacy concerns. So we study the personalized differential privacy 
(PDP) model, where each user may have a different privacy parameter. Within 
this framework, we present the personalized truncation mechanism which is the 
first PDP mechanism with an explicit utility guarantee. This mechanism is 
designed to accommodate a broad class of select-join-aggregate (SJA) queries 
over relational databases, even under foreign-key constraints.

Finally, in real-world contexts, users often face challenges in trusting 
service providers. Consequently, a common approach is that each user 
privatizes their data by themselves before sending it to an untrusted data 
analyzer, which is known as the local model of DP. We further extend our 
study of personalized privacy into the local DP model by developing advanced 
algorithms for sum estimation.


Date:                   Thursday, 23 January 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Ke Yi (Supervisor)
                        Prof. Qiong Luo (Chairperson)
                        Prof. Dimitris Papadias
                        Prof. Raymond Wong