Citizen Privacy Framework: Case of a Fuzzy-based Recommender System for Political Participation

121 Pages·2022·2.732 MB·English

by  Aigul Kaskina

Fuzzy Management Methods
Series Editors: Andreas Meier · Witold Pedrycz · Edy Portmann
Aigul Kaskina
Citizen
Privacy
Framework
Case of a Fuzzy-based Recommender
System for Political Participation
Fuzzy Management Methods
SeriesEditors
AndreasMeier,Fribourg,Switzerland
WitoldPedrycz,Edmonton,Canada
EdyPortmann,Bern,Switzerland
With today’sinformationoverload,it hasbecomeincreasinglydifficultto analyze
the huge amounts of data and to generate appropriate management decisions.
Furthermore, the data are often imprecise and will include both quantitative
and qualitative elements. For these reasons it is important to extend traditional
decision making processes by adding intuitive reasoning, human subjectivity and
imprecision.To dealwithuncertainty,vagueness,andimprecision,LotfiA.Zadeh
introduced fuzzy sets and fuzzy logic. In this book series “Fuzzy Management
Methods” fuzzy logic is applied to extend portfolio analysis, scoring methods,
customer relationship management, performance measurement, web reputation,
web analytics and controlling, community marketing and other business domains
to improvemanagerialdecisions. Thus, fuzzy logic can be seen as a management
methodwhereappropriateconcepts,softwaretoolsandlanguagesbuildapowerful
instrumentforanalyzingandcontrollingthebusiness.
Aigul Kaskina
Citizen Privacy Framework
Case of a Fuzzy-based Recommender System
for Political Participation
AigulKaskina
DepartmentofInformatics
UniversityofFribourg
Fribourg,Switzerland
ISSN2196-4130 ISSN2196-4149 (electronic)
FuzzyManagementMethods
ISBN978-3-031-06020-5 ISBN978-3-031-06021-2 (eBook)
https://doi.org/10.1007/978-3-031-06021-2
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To myparentsandAndreasMeier in
recognitionof theirsupport.
Foreword
Privacyhasbecomeanincreasinglyrelevanttopicinthefieldofsocialinformation
systems, and particularly in the field of eDemocracy, where applications increas-
ingly gather tremendousamountsof user data as input to the democratic process,
for political discussion, or for recommendation purposes. This data may pose a
severethreattouserprivacy,forexample,ifaccessedbyuntrustedparties,orused
inappropriately.Inessence,usersofthesesystemsfaceavexingtradeoff:disclosing
theirinformationwouldincreasetheir abilitytoparticipateinthe onlinediscourse
that these systems enable, but privacyconcernsurge them to refrain from sharing
morethananominalamountofinformationwiththesesystems.
Aspeoplevarysubstantiallywithregardtohowtheyvaluethistradeoff,privacy
advocates suggest giving users an informed choice regarding the disclosure and
use of their personalinformation.Unfortunately,the privacyimplications of most
modern information systems are so complex that even the most motivated users
are unable to take effective control over their privacy settings without reaching
thelimits oftheircognitiveabilities. To overcomethese problems,researchersare
increasinglyfocusingon user-tailoredprivacy asa meansto providepersonalized
privacydecisionsupport.Broadlyspeaking,user-tailoredprivacyfirstcreatesauser
modelbasedonacontextualizedunderstandingofusers’privacybehaviorsandthen
uses this model to adapt the default privacy settings, privacynotices, the privacy-
setting interface, and/or the personalization mechanism to each individual user’s
preferences.
The academic understanding of user models for privacy has evolved from
unidimensional to multidimensional to profile-based models. In most systems,
however, the privacy preferences of users are complex and varied to an extent
that they cannot be captured in a small discrete set of profiles. This is the central
observation upon which Dr. Aigul Kaskina based her thesis A Fuzzy-Based User
Privacy Framework and Recommender System: Case of a Platform for Political
Participation,whichispublishedinthisbookandforwhichshereceivedherPhD
in 2018 from the Faculty of Science at the Universityof Fribourgin Switzerland.
Dr. Kaskina’s work proposes fuzzy clustering as a much-needed solution to this
problem, since it serves as a compromise between the simplicity and elegance of
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viii Foreword
simple profiles and the advanced ability to capture each individual user’s privacy
preferencesofrecommendationapproachessuchascollaborativefiltering.
While the broader goal of this book is to develop a conceptual framework
thatenhancescitizens’ privacyin eDemocracyby generatingpersonalizedprivacy
recommendations,itcanprimarilybecommendedforitsdirect,real-lifeapplication
of user-tailored privacy in ParticipaIntelligente, an online political participation
platform in Ecuador. Although political discourse is more fruitful in audience-
restricted environments, the user study presented in this book finds substantial
amount of variation in the privacy settings of 391 users of this application. In
fact, many of these users do not restrict the visibility of their contributions and
interactions on the platform, most likely because of a lack of knowledge and/or
engagement. To help these users, Dr. Kaskina implements a fuzzy-based privacy
recommender, which analyzes the user’s current privacy settings and provides a
recommendationfor“better”settings.Effectively,thissystemleveragestheefforts
of users who possess sufficient knowledge and motivation to spend effort to set
their privacy settings to provide personalized recommendations to users who do
not possess such knowledge or motivation. The accuracy of multiple clustering
methodsandclustergranularitiesisconsidered,andthebookculminatesinauser-
centriccomparisonof“sharp”(onaveragemuchdifferentfromusers’ownsettings)
and“soft”(nottoodifferentfromusers’ownprofiles)recommendationapproaches
againstusers’manuallyselectedsettings.Particularly,the“soft”recommendations
resultinlowerreactancebutalsoasomewhatloweroutcomesatisfaction,whilethe
“sharp”recommendationsscoreequaltousers’ownsettingsonoutcomesatisfaction
butperformworseonallothermetrics.
Toconclude,thisbookcontainstrailblazingworkintheuseoffuzzyclustering
forprivacypreferenceprediction.Istronglyrecommendthisbooktousermodeling
researchers interested in a practical application of fuzzy clustering, to privacy
researchers interested in the state of the art in privacy modeling, and to system
designers and service providers in eDemocracy who want to learn about ways to
buildsystemsthatrespecttheprivacyoftheirusers.
SchoolofComputing Dr.BartP.Knijnenburg
ClemsonUniversity
UnitedStatesofAmerica
Acknowledgments
egocurious,ergosum.jesuiscurieux,doncjesuis
I would like to acknowledge the immense support from my supervisor, Prof Dr
Andreas Meier, who helped make this thesis feasible. Thank you, Andreas, for
supporting me in developing my capabilities as an independent researcher. This
work was another step on the way of discovering and finding myself. I was also
surrounded with the continuous support and mentoring from my dear colleagues
Luis Teran, Marcel Wehrle, Alexander Denzler, Dac Hoa and others, for which I
am extremely grateful. Your valuable advice encouraged me to move further. A
specialthankyougoestoLuisTeranandMarcelWehrlefortheirtechnicalsupport
duringtheresearchproject.AvaluableengagementfromBartKnijnenburgandEdy
Portmann profoundlycontributed to the accomplishmentof the thesis. Your great
expertiseandscientificenthusiasmwasincrediblyinspiringandshapedmyresearch
mind.Thankyouforyoursupportandinsightfulcollaboration.Itwasalsoapleasure
to collaborate with outstanding students Nevena Radovanovic, Sonia Parani and
Luka Hamza. Thank you for working on common projects where I have learned
new things since working with you. To Sylviane Pilloud and Elisabeth Brügger,I
giveasincerethankyouforhelpingmeduringmystayandtheteachingactivitiesat
the University of Fribourg. I am glad to have met my friends with whom I have
enjoyed these 4 years of research, in particular, Lorena Recalde, Ximena Jativa
Sierra,MarcoSchneiter,LizaZimmerman,MelanieBoninsegni,MinhTueNguyen,
RezaGhaiumyAnarakyandotherdearfriendsfromallovertheworld.Finally,an
endlessgratitudeandlovetomyparentsforbringingmeintothisbeautifulworld.
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Contents
1 Introduction .................................................................. 1
1.1 Motivation .............................................................. 1
1.2 Objectives............................................................... 2
1.3 ResearchQuestions..................................................... 2
1.4 ResearchMethods...................................................... 3
1.5 ThesisOutline .......................................................... 5
1.6 OwnResearchContribution ........................................... 6
References..................................................................... 7
2 InsightsintoPrivacyResearch ............................................. 9
2.1 EconomicsofPrivacy—aRationalHuman ........................... 9
2.1.1 PrivacyCalculus................................................ 9
2.1.2 DeathtoPrivacyCalculus?.................................... 10
2.1.3 PrivacyParadox................................................ 11
2.2 BeyondRationality—HumanCognition .............................. 13
2.2.1 CognitiveHeuristicsBehindDisclosureDecisions........... 13
2.2.2 EnforcedbyIndividualCharacteristics? ...................... 14
2.3 AutomatingUserPrivacySupport..................................... 15
References..................................................................... 17
3 CitizenPrivacyProfileFramework........................................ 21
3.1 ConceptualDevelopment .............................................. 21
3.1.1 OverviewofExistingPrivacyFrameworks................... 21
3.1.2 CitizenPrivacyProfile(CPP)Framework .................... 24
3.1.3 EvaluationofCPPFramework ................................ 29
3.2 ImplementationofCPP ................................................ 32
3.2.1 PlatformDescription........................................... 32
3.2.2 PrivacySettingsFunctionality................................. 34
3.2.3 PrivacyProfilesExtraction..................................... 37
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