[CS] [defense] Xu/Dissertation Defense/Nov 6, 2020

[Tricia Baclawski]

https://uchicago.zoom.us/j/92335928701?pwd=dzhYSlJGOGRUamRxM3p4WGYwSXA1QT09
Password: 569803

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Min Xu

Date:  Friday, November 6, 2020

Time:  10:00 AM

Place:  via zoom

Title: Towards Better Data Privacy and Utility in the Untrusted Cloud

Abstract:
Users data are stored and utilized in the cloud for various purposes.
How to best utilize these data while at the same time preserving the
privacy of their owners is a challenging problem. In this
dissertation, we focus on three important cloud applications, and
propose solutions to enhance the privacy-utility trade-offs of the
existing ones. The first application is the federated SQL processing,
where multiple mutually-untrusted data owners hold valuable data of
their own, and want to execute joint SQL queries on these data without
leaking information about individual records in their own shares. The
second one is the cloud data collection and analysis, where services
collect their users data, with proper privacy guarantees, and want to
enable expressive and accurate analysis on the collected data. The
last one is the end-to-end encrypted data retrieval, where a single
data owner outsources her end-to-end encrypted data to the cloud, and,
later, wants to retrieve some of them that are most relevant to the
keyword queries requests. After comprehensive literature review of the
existing solutions, we realize that the privacy-utility trade-offs of
state of the art can be substantially improved. For federated SQL
processing, existing solutions leverage trusted hardware for efficient
and secure computations in the cloud, while subsequent work
demonstrates the devastating side-channel vulnerability of these
solutions. We mitigate such vulnerability to improve the existing
solutions. For data collection and analysis, existing solutions do not
support joint analysis across data collected by separate services, and
the supported analytics is limited, i.e., counting frequency of
certain value. We propose new mecha- nisms and estimation algorithms
to achieve better utility on the collected data. For end-to-end en-
crypted data retrieval, existing solutions are vulnerable to the
powerful yet practical file-injection attacks, and we propose new
constructions that can defend against such attacks, with practical
performance. We thoroughly analyze the privacy and utility of the
proposed solutions, when necessary. We also implement prototypes for
all the solutions, and conduct extensive evaluations to show the
performance of our proposed solutions.

Min's advisor is Prof. David Cash

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://newtraell.cs.uchicago.edu/phd/phd_announcements#xum

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Tricia Baclawski
Student Affairs Administrator
Computer Science Department
5730 S. Ellis - Room 350
Chicago, IL 60637
pbaclawski at uchicago.edu
(773) 702-6854
/pronouns: she, her, hers/
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=