Usability Heuristics for fast crime Data anonymization in resource-constrained contexts

175 Pages·2017·6.81 MB·English

Usability Heuristics for Fast Crime Data Anonymization in
Resource-constrained Contexts
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Aderonke Busayo, SAKPERE ([email protected])
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Submittediin fulfillment of the Requirements
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Doctor of Philosophy
Faculty of Science
University of CapeTown
Friday 26th January, 2018
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The copyright of this thesis vests in the author. No
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quotation from it or information derived from it is to be

published without full acknowledgeement of the source.
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The thesis is to be used for private study or non-
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commercial research purposes only.

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Published by the Universit y of Cape Town (UCT) in terms
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of the non-exclusive license granted to UCT by the author.
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CERTIFICATION
As the candidate’s supervisor, I have approved this thesis for submission.
Supervisor: Dr Anne V.D.M. Kayem
Signature:
Date:
DECLARATION
I declare that this thesis is my own work. Where collaboration with other people has taken place, or
material generated by other researchers is included, the parties and/or materials are indicated in the
acknowledgements or are explicitly acknowledged through references as appropriate.
This thesis is being submitted for the doctoral degree in Computer Science at the University of Cape
Town. It has not been submitted to any other university for any other degree or examination.
Aderonke Busayo Sakpere Date
ABSTRACT
This thesis considers the case of mobile crime-reporting systems that have emerged as an effective and
efficient data collection method in low and middle income countries. Analyzing the data, can be helpful
in addressing crime. Since law enforcement agencies in resource-constrained context typically do not
have the expertise to handle these tasks, a cost-effective strategy is to outsource the data analytics
tasks to third-party service providers. However, because of the sensitivity of the data, it is expedient to
consider the issue of privacy. More specifically, this thesis considers the issue of finding low-intensive
computationalsolutionstoprotectingthedataevenfroman“honest-but-curious"serviceprovider, while
at the same time generating datasets that can be queried efficiently and reliably.
This thesis offers a three-pronged solution approach. Firstly, the creation of a mobile application to
facilitate crime reporting in a usable, secure and privacy-preserving manner. The second step proposes a
streaming data anonymization algorithm, which analyses reported data based on occurrence rate rather
than at a preset time on a static repository. Finally, in the third step the concept of using privacy
preferences in creating anonymized datasets was considered. By taking into account user preferences
the efficiency of the anonymization process is improved upon, which is beneficial in enabling fast data
anonymization.
Results from the prototype implementation and usability tests indicate that having a usable and covet
crime-reporting application encourages users to declare crime occurrences. Anonymizing streaming data
contributes to faster crime resolution times, and user privacy preferences are helpful in relaxing privacy
constraints, which makes for more usable data from the querying perspective.
This research presents considerable evidence that the concept of a three-pronged solution to addressing
the issue of anonymity during crime reporting in a resource-constrained environment is promising. This
solution can further assist the law enforcement agencies to partner with third party in deriving useful
crimepatternknowledgewithoutinfringingonusers’privacy. Inthefuture,thisresearchcanbeextended
to more than one low-income or middle-income countries.
Keywords: CrimeReporting,Anonymization,SlidingWindowResizing,UserPrivacy,Resource-Constrained
Environment.
Dedication
To my parents, who inspired me and provided a platform for my career dreams to come to fruition. Its
sure not a coincidence having my PhD 35 years after my daddy’s own.
To my late uncle, ’Remilekun and his family’, the seed you have sown in my father’s life birthed this.
Its interesting having my PhD about 55 years after yours.
To my husband, who sacrificed a lot to support my research and quest for knowledge up to the finishing
line.
ACKNOWLEDGEMENTS
I appreciate God Almighty who has made it possible for me to complete this research work successfully
against all odds.
Mysincereappreciationgoestomysupervisor,DrAnneV.D.M.Kayem,forexposingmetotherudiments
of research. I lack words to express my gratitude for her constructive criticism at every phase of this
research, her deep technical input, her creative writing style which I have adopted for this thesis, moral
support and vast knowledge and values I have acquired as a result of my relationship with her. In
truth, my life has been transformed as a result of my contact with her. Lastly, I thank her for the
patience she exercised during the challenging period of this research. I am highly grateful to Prof James
Gain for stepping in towards the end of my study when my supervisor left UCT. Prof, thank you for
the sacrifices you made to ensure i finish strong. Besides my supervisors, I would like to thank Prof.
StephenWolthusenfromNTNU,NorwayandthefollowingUCTlecturers: ProfHusseinSulemanandDr
Melissa Densmore, for insightful comments. My sincere thanks also go to the Hasso Plattner Institute,
University of Ibadan, IFIP, CROSSFYRE and INSTICC for the travel grant and scholarship given to me
during my research.
I am grateful to all my colleagues in the infosec research group, ICT4D research laboratory and room
300. To my colleagues at the University of Ibadan (Nigeria), thank you for your support. In particular
I would like to thank Dr Wunmi Isafiade, Dr Ayo Periola and Dr Olu Onifade for helping me proofread
some chapters; Thabo Ndlovu who assisted in the design and implementation of the CryHelp App and
Sadiq Hassan for his assistance during data analysis.
Last but not least, I would like to thank my family: parents, in-laws, siblings, uncles, aunts and pastors,
for their unflinching support throughout my research and life in general, with a special mention of my
husband, Wilson, for his sacrifices.
Contents
DECLARATION
List of Figures
List of Figures
1 INTRODUCTION 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Context and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Trends in Data Anonymization . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.7 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 State of the Art 14
2.1 Motivation for Data Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Data Anonymization Rationale and Concept . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Data Anonymization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Perturbative Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Non-Perturbative Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 k-anonymity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Classification of k-Anonymity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.2 ‘-Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.3 From ‘-Diversity to t-Closeness . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 New Trends in Privacy Preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6 Information Loss Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 Data Stream Anonymization Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.8 Data Stream Anonymization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.8.1 Perturbative Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.8.2 Non-Perturbative Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.8.3 Hierarchy-Based Generalization of Data Stream Anonymization . . . . . . . . . . 32
2.8.4 Hierarchy-Free Generalization of Data Stream Anonymization . . . . . . . . . . 35
2.9 Contributions and Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Data Anonymization Framework 43
3.1 General Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 User Layer: CryHelp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.1 Requirement Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.2 User Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Prototypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49