Provable and Practical Location Privacy for Vehicular and Mobile Systems Raluca Ada Popa

91 Pages·2010·0.78 MB·English

Provable and Practical Location Privacy
for Vehicular and Mobile Systems
by
Raluca Ada Popa
B.S., Computer Science and Engineering (2009)
B.S., Mathematics (2009)
Massachusetts Institute of Technology
Submitted to the Department of Electrical Engineering and Computer Science
In partial fulfillment of the requirements for the degree of
Master of Engineering in Electrical Engineering and Computer Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 2010
(cid:13)c Massachusetts Institute of Technology 2010. All rights reserved.
Author............................................................................
Department of Electrical Engineering and Computer Science
May 7, 2010
Certified by........................................................................
Hari Balakrishnan
Professor of Electrical Engineering and Computer Science
Thesis Supervisor
Accepted by.......................................................................
Christopher J. Terman
Chairman, Department Committee on Graduate Students
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Provable and Practical Location Privacy
for Vehicular and Mobile Systems
by
Raluca Ada Popa
Submitted to the Department of Electrical Engineering and Computer Science
May 7, 2010
In partial fulfillment of the requirements for the degree of
Master of Engineering in Electrical Engineering and Computer Science
Abstract
In recent years, there has been a rapid evolution of location-based vehicular and mobile ser-
vices(e.g.,electronictolling,congestionpricing,trafficstatistics,insurancepricing,location-
basedsocialapplications),whichpromisetremendousbenefitstousers. Unfortunately,most
such systems pose a serious threat to the location privacy of users because they track each
individual’s path.
A question that arises naturally is how can we preserve location privacy of users while
maintainingthebenefitsofsuchservices? Inthisthesis,weaddressthisquestionbytackling
two general problems that are the foundation of many of the aforementioned services. The
first problem is how to enable an untrusted server to compute agreed-upon functions on
a specific user’s path without learning the user’s path. We address this problem in a
system called VPriv. VPriv supports a variety of applications including electronic tolling,
congestion pricing, insurance premium computation, and some kinds of social applications.
The second problem is how to enable an untrusted server to compute aggregate statistics
over all users’ paths without learning any specific user’s path. We tackle this problem
in a system called PrivStats. With PrivStats, one can compute statistics such as traffic
statistics (e.g., average speed at an intersection, average delay on a road, number of drivers
at a location) or average ratings of a location in a social application. The computation
and threat models for VPriv and PrivStats are different, and required markedly different
solutions. For both systems, we provide formal definitions of location privacy and prove
that our protocols achieve these definitions. We implemented and evaluated both systems,
and concluded that they are practical on commodity hardware and smartphones.
Thesis Supervisor: Hari Balakrishnan
Title: Professor of Electrical Engineering and Computer Science
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Pentru Tatiana, mama mea.
Mul¸tumesc.
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Acknowledgments
This research was performed under the supervision of Professor Hari Balakrishnan and it
is joint work with Professor Andrew J. Blumberg, from University of Texas, Austin. The
work on VPriv was published at the 18th Usenix Security Symposium and the paper on
PrivStats is in submission.
I would like to thank Hari for being an amazing advisor. I was greatly inspired by his
excitement, vision, and creativity. Working with Hari was a great learning experience and
a lot of fun. I am especially thankful for his constant and enthusiastic support.
My thesis work would not have been as rewarding without Andrew. I enjoyed a lot our
persistent attempts to solve a problem and our weekly reading sessions on cryptography
topics. Thanks a lot for your help and enthusiasm for the project!
I am deeply grateful to Barbara Liskov and Ron Rivest, who were my first research
mentors. I wish to thank them for the many things they taught me, for their generosity
in sharing their time with me, and for their support. They have greatly inspired me and
became my role models.
I am profoundly indebted to Liviu and Cristina Iftode for their generous support and
advice with moving for college to the US.
I would like to thank Alessandro Chiesa, my boyfriend, for his support, encouragement,
and care. He is also a great friend and technical collaborator.
Thanks to my friend Doris Dobi for our enthusiastic discussions and lasting friendship.
IamgratefultoSilvioMicaliforhavingtaughtmecryptographyandtoFransKaashoek
forhisadvice. ThemembersoftheCarTelproject,especiallySamMadden,JakobEriksson,
Sejoon Lim, and Arvind Thiagarajan provided access to data traces and useful feedback.
In addition, I thank Seth Riney of PlanetTran for his partnership with the CarTel project,
which enabled the collection of data used to evaluate the protocols in my thesis. David
Andersen, Sharon Goldberg, Ramki Gummadi, Sachin Katti, Petros Maniatis, and the
members of the PMG group at MIT have provided many useful comments. Also thanks to
Yuanyuan Zhou and Nickolai Zeldovich.
This work was supported in part by the National Science Foundation under grants
CNS-0716273 and CNS-0520032.
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Overview
1 Introduction 17
2 Related Work 23
3 Cryptographic Background 27
4 Model 31
5 VPriv 39
6 PrivStats 57
7 Other Applications 75
8 Conclusions 79
Appendix 80
Statement of Originality 85
Bibliography 85
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10 OVERVIEW