Enabling Accurate Analysis of Private Network Data

201 Pages·2014·1.9 MB·English

by  Michael Hay

University of Massachusetts Amherst
ScholarWorks@UMass Amherst
Open Access Dissertations
9-2010
Enabling Accurate Analysis of Private Network
Data
Michael Hay
University of Massachusetts Amherst, [email protected]
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ENABLING ACCURATE ANALYSIS OF PRIVATE
NETWORK DATA
A Dissertation Presented
by
MICHAEL G. HAY
Submitted to the Graduate School of the
University of Massachusetts Amherst in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
September 2010
Computer Science
(cid:13)c Copyright by Michael G. Hay 2010
All Rights Reserved
ENABLING ACCURATE ANALYSIS OF PRIVATE
NETWORK DATA
A Dissertation Presented
by
MICHAEL G. HAY
Approved as to style and content by:
Gerome Miklau, Co-chair
David Jensen, Co-chair
Don Towsley, Member
Andrew Papachristos, Member
Andrew G. Barto, Department Chair
Computer Science
ACKNOWLEDGMENTS
This work would not have been possible without the support and mentorship of
David Jensen and Gerome Miklau.
David has been a generous and accommodating advisor. He gave me the latitude
to explore the world of research freely and he taught me the methods that made
the exploration fruitful. I have found his research methods invaluable in practice,
but more importantly, they were foundational to my success, because they made the
mysterious craft of research accessible to me. In addition to his teachings, I am
grateful for his patience and encouragement. David also oriented and prepared me
for a career in research, giving me valuable perspective on the challenges and rewards
of such a career.
Gerome introduced me to the topic of data privacy and deserves much credit for
guiding the effort that went into this dissertation. By working closely with me, he
taught me how to formalize a problem and how to transform insights into substan-
tive contributions. He helped me avoid doubtful thinking and embrace the intrinsic
uncertainty of research with optimism. He also taught me the art of scholarly pre-
sentation: how to write and speak with precision and clarity, how to structure an
argument, and how to embrace the mindset of the intended audience. I have been
the grateful recipient of his advice: he is gentle, honest, and brings to every problem
a clarity of mind that is enviable.
In addition to Gerome and David, I am grateful for the valuable input of the other
members of my committee: Don Towsley contributed theoretical results to our early
work and gave insightful comments on the rest; Andrew Papachristos guided me to
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relevant sociology literature and supplied a valuable perspective as a potential end
user.
The work in this dissertation is a product of the hard work and valuable insights
of many people. I am grateful to my coauthors for their contributions: Gerome
Miklau, David Jensen, Dan Suciu, Vibhor Rastogi, Chao Li, Don Towsley, Andrew
McGregor, Philipp Weis, and Siddharth Srivastava. In addition, I am thankful to
Vibhor for many thoughtful discussions and also for his friendship.
I have benefited greatly from interacting with the members, past and present, of
the Knowledge Discovery Laboratory and the Database Group. Jen Neville and Andy
Fast gave me hope and encouragement that a Ph.D. is within my reach. Brian Gal-
lagher provided valuable friendship. Lisa Friedland entertained countless impromptu
requests to act as a sounding board for my ideas, as well as my gripes. Chao Li has
been a great collaborator and a memorable travel partner.
The Computer Science Department at UMass Amherst is a special place and I
have learned so much as a student here. I am especially grateful to Micah Adler,
Yanlei Diao, Neil Immerman, and Andrew McCallum for their instruction and to
James Allan, Deb Bergeron, Rachel Lavery, Leeanne LeClerc, and Sharon Mallory
for their support.
Finally, I would not have completed this journey without the love and support
of family and friends. Al and Buffy have been incredibly supportive, inviting us to
enjoy several wonderful beach vacations, welcoming us into their home for a summer,
and providing a safe haven for Carey and the boys when paper deadlines neared. My
mom has helped keep a roof over our heads and yummy, organic food in our fridge,
and she has been a sweet Nana to our boys and a lifeline to us. My Dad gave me
encouragement to stick with it when I needed it most and has been incredibly helpful,
especially with our transition to Ithaca. Nancy, Chris, and Clara have provided some
memorable excursions, filled with tasty treats and big screen fun. Declan, Jesse,
v
Tanner, Dylan, Talia, Rosalie and their respective parents have been great friends
and make us sad to leave Amherst. My boys motivated me to work hard and brought
me joy at home. And Carey, you have been there from the beginning and by my side
for every twist of the road. It has been a pleasant journey indeed and you made it
so. Thank you.
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ABSTRACT
ENABLING ACCURATE ANALYSIS OF PRIVATE
NETWORK DATA
SEPTEMBER 2010
MICHAEL G. HAY
A.B., DARTMOUTH COLLEGE
M.S., UNIVERSITY OF MASSACHUSETTS AMHERST
Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST
Directed by: Professor Gerome Miklau and Professor David Jensen
This dissertation addresses the challenge of enabling accurate analysis of network
data while ensuring the protection of network participants’ privacy. This is an impor-
tant problem: massive amounts of data are being collected (facebook activity, email
correspondence, cell phone records), there is huge interest in analyzing the data, but
the data is not being shared due to concerns about privacy. Despite much research
in privacy-preserving data analysis, existing technologies fail to provide a solution
because they were designed for tables, not networks, and cannot be easily adapted to
handle the complexities of network data.
We develop several technologies that advance us toward our goal. First, we de-
velop a framework for assessing the risk of publishing a network that has been “an-
onymized.” Using this framework, we show that only a small amount of background
knowledge about local network structure is needed to re-identify an “anonymous” in-
dividual. This motivates our second contribution: an algorithm that transforms the
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structure of the network to provably lower re-identification risk. In comparison with
other algorithms, we show that our approach more accurately preserves important
features of the network topology. Finally, we consider an alternative paradigm, in
which the analyst can analyze private data through a carefully controlled query in-
terface. We show that the degree sequence of a network can be accurately estimated
under strong guarantees of privacy.
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CONTENTS
Page
ACKNOWLEDGMENTS ............................................. iv
ABSTRACT......................................................... vii
LIST OF TABLES ...................................................xiii
LIST OF FIGURES..................................................xiv
CHAPTER
1. INTRODUCTION ................................................. 1
1.1 Problem setting of prior work: privacy in tabular data.................2
1.2 Our problem setting: privacy in network data ........................9
1.3 Overview of contributions.........................................16
1.3.1 Assessing the risk of network data publication.................16
1.3.2 Mitigating risk through network transformation ...............18
1.3.3 Estimating network statistics under strong privacy.............19
2. BACKGROUND ................................................. 23
2.1 K-anonymity ...................................................23
2.2 Differential privacy ..............................................28
2.3 Differentially private query answering ..............................31
2.4 Differential privacy for graphs .....................................33
2.5 Network analyses and statistics ....................................36
3. ASSESSING RE-IDENTIFICATION RISK ....................... 40
3.1 Modeling the adversary...........................................42
3.1.1 Naive anonymization ......................................42
3.1.2 Threats ..................................................43
3.1.3 Anonymity through structural similarity......................46
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