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

Foreword
Acknowledgments
Contents
List of Figures
List of Tables
List of Acronyms
1 Introduction
	1.1 Motivation
	1.2 Objectives
	1.3 Research Questions
	1.4 Research Methods
	1.5 Thesis Outline
	1.6 Own Research Contribution
	References
2 Insights into Privacy Research
	2.1 Economics of Privacy—a Rational Human
		2.1.1 Privacy Calculus
		2.1.2 Death to Privacy Calculus?
		2.1.3 Privacy Paradox
	2.2 Beyond Rationality—Human Cognition
		2.2.1 Cognitive Heuristics Behind Disclosure Decisions
		2.2.2 Enforced by Individual Characteristics?
	2.3 Automating User Privacy Support
	References
3 Citizen Privacy Profile Framework
	3.1 Conceptual Development
		3.1.1 Overview of Existing Privacy Frameworks
			Liu et al. framework
			Aimeur et al. Framework
			Knijnenburg et al. Framework
		3.1.2 Citizen Privacy Profile (CPP) Framework
			Voting Advice Applications
			Citizen Privacy Profile Framework
		3.1.3 Evaluation of CPP Framework
	3.2 Implementation of CPP
		3.2.1 Platform Description
			Platform Blocks
			Type of Content
			User Roles
		3.2.2 Privacy Settings Functionality
			Information: Data Levels
			Social Network: Audience Levels
		3.2.3 Privacy Profiles Extraction
	3.3 User Privacy Profiles Modelling
		3.3.1 Fuzzy Clustering
			Dataset Clustering Tendency
			Fuzzy C-Means Clustering
			Partitioning Around Medoids Clustering
			Measuring Distances
		3.3.2 Evaluation of Clustering Validity
			Partition Coefficient (PC)
			Partition Entropy (PE)
			Modified Partition Coefficient (MPC)
			Xie and Beni Index (XBI)
			Crisp Silhouette (CS)
			Fuzzy Silhouette (FS)
			Analysis of the Validation Results
		3.3.3 Discussion of Fuzzy Privacy Profiles
	3.4 User Evaluation of Privacy Behaviour
		3.4.1 Objective System Aspect
		3.4.2 User Experience
		3.4.3 Personal Characteristics
		3.4.4 Evaluation Analysis
			Independent Variables Analysis
			Combined Variable Analysis
	3.5 Conclusions
	References
4 Fuzzy-Based Privacy Settings Recommender System
	4.1 Conceptual Development
		4.1.1 Overview of the Existing Systems
			Privacy Wizard
			YourPrivacyProtector
			Personalised Privacy Assistant
		4.1.2 Fuzzy-Based Privacy Settings Recommender System (FPRS)
	4.2 Implementation of FPRS
		4.2.1 Prototype Architecture
		4.2.2 Recommendations Calculation
		4.2.3 Evaluation of Recommendations Accuracy
	4.3 User Evaluation of Privacy Recommendations
		4.3.1 Objective System Aspects
		4.3.2 Subjective System Aspects
			Perceived Recommendation Sharpness
			Perceived Recommendation Quality
		4.3.3 User Experience
			Persuasion Effect
			Reactance Effect
			Outcome Satisfaction
		4.3.4 Personal Characteristics
			Inconsistent/Consistent Privacy Behaviour
		4.3.5 Evaluation Setup
			Experimental Manipulations
			Experimental Procedures
			Defining Measurements
		4.3.6 Evaluation Analysis
	4.4 Conclusions
	References
5 Conclusions
	5.1 Discussions
		5.1.1 Implication to the Public Need
		5.1.2 Implication to the Knowledge Base
		5.1.3 Limitations of the Research
		5.1.4 Answering Research Questions
	5.2 Future Outlook
	References
Appendix A
	A.1 Privacy Profiles Extraction
Appendix B
	B.1 User Evaluation Survey for Privacy Behaviour
Appendix C
	C.1 User Evaluation Setup