Technical Solutions for Privacy- Enhanced Personalization

Technical Solutions for Privacy- Enhanced Personalization

Yang Wang (University of California, Irvine, USA)
DOI: 10.4018/978-1-60566-032-5.ch017
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Abstract

This chapter presents a first-of-its-kind survey that systematically analyzes existing privacy-enhanced personalization (PEP) solutions and their underlying privacy protection techniques. The evaluation is based on an analytical framework of privacy-enhancing technologies, an earlier work of the authors. More specifically, we critically examine whether each PEP solution satisfies the privacy principles and addresses the privacy concerns that have been uncovered in the context of personalization. The chapter aims at helping researchers better understand the technical underpinnings, practical efficacies and limitations of existing PEP solutions, and at inspiring and developing future PEP solutions by outlining several promising research directions based on our findings.
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Introduction

Privacy and personalization are currently at odds (Kobsa, 2002, 2007a, 2007b; Teltzrow & Kobsa, 2004; Wang & Kobsa, 2006). For instance, online shoppers who value that an online bookstore can give them personalized recommendations based on what books they bought in the past may wonder whether their purchase records will be kept truly confidential in all future. Online searchers who are pleased that a search engine disambiguates their queries and delivers search results geared towards their genuine interests may feel uneasy that this entails recording all their past search terms. Students who appreciate that a personalized tutoring system can provide individualized instruction based on a detailed model of each student’s understanding of the different learning concepts may wonder whether anyone else besides the system will have access to these models of what they know and don’t know.

Various technical solutions have been proposed to safeguard users’ privacy while still providing satisfactory personalization, e.g., on web retail or product recommendation sites. Technical solutions for privacy protection represent a special kind of so-called Privacy-Enhancing Technologies (PETs). In (Wang & Kobsa, forthcoming), we propose an evaluation framework for PETs that considers the following dimensions:

  • 1.

    What high-level principles the solution follows: We identify a set of fundamental privacy principles that underlie various privacy laws and regulations and treat them as high-level guidelines for enhancing privacy.

  • 2.

    What privacy concerns the solution addresses: We analyze privacy solutions along major privacy concerns that were identified in the literature.

  • 3.

    What basic privacy-enhancing techniques the solution employs: We look at the technical characteristics of privacy solutions, to critically analyze their effectiveness in safeguarding privacy and supporting personalization.

The rest of this chapter is organized as follows. Firstly, we describe and categorize major privacy principles from privacy laws as well as other desirable principles in the context of privacy protection (we thereby largely follow (Wang & Kobsa, forthcoming)). Secondly, we discuss privacy concerns and how different privacy principles address them. Thirdly, as the central contribution of this chapter, we describe the techniques that have been used in the main types of privacy-enhanced personalization solutions, and how they relate to the major privacy concerns and privacy principles. Fourthly, we discuss findings from this analysis. Finally, we conclude with future research directions.

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Privacy Principles

Privacy legislation and regulation is usually based on more fundamental privacy principles. In our framework, we select a comprehensive set of major principles from our survey of over 40 international privacy laws and regulations (Kobsa, 2007b; Wang, Zhaoqi, & Kobsa, 2006). Any principle manifested in these privacy laws and regulations was included in our framework if it has impacts on how web-based personalized systems operate. Besides, we also define or identify other principles/properties that are desirable for privacy enhancement and personalization. Additional principles may possibly need to be added in the future, as new personalization technologies with new privacy threats emerge or the concept of privacy evolves. Below we list our principles, grouped by their provenance.

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Table of Contents
Foreword
Barry Smyth
Preface
Constantinos Mourlas, Panagiotis Germanakos
Acknowledgment
Constantinos Mourlas, Panagiotis Germanakos
Chapter 1
Nikos Tsianos, Panagiotis Germanakos, Zacharias Lekkas, Constantinos Mourlas
The plethora of information and services as well as the complicated nature of most Web structures intensify the navigational difficulties that arise... Sample PDF
Assessment of Human Factors in Adaptive Hypermedia Environments
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Chapter 2
Barry Smyth
Everyday hundreds of millions of users turn to the World-Wide Web as their primary source of information during their educational, business and... Sample PDF
Case Studies in Adaptive Information Access: Navigation, Search, and Recommendation
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Chapter 3
Sherry Y. Chen
Web-based instruction is prevalent in educational settings. However, many issues still remain to be investigated. In particular, it is still open... Sample PDF
The Effects of Human Factors on the Use of Web-Based Instruction
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Chapter 4
Gulden Uchyigit
Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization... Sample PDF
The Next Generation of Personalization Techniques
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Chapter 5
Nancy Alonistioti
This chapter introduces context-driven personalisation of service provision based on a middleware architectural approach. It describes the emerging... Sample PDF
Advanced Middleware Architectural Aspects for Personalised Leading-Edge Services
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Chapter 6
Syed Sibte Raza Abidi
This chapter introduces intelligent information personalization as an approach to personalize the webbased information retrieval experiences based... Sample PDF
Intelligent Information Personalization: From Issues to Strategies
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Chapter 7
Babis Magoutas
This chapter introduces a semantically adaptive interface as a means of measuring the quality of egovernment portals, based on user feedback. The... Sample PDF
A Semantically Adaptive Interface for Measuring Portal Quality in E-Government
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Chapter 8
Fabio Grandi, Federica Mandreoli, Riccardo Martoglia, Enrico Ronchetti, Maria Rita Scalas
While the World Wide Web user is suffering form the disease caused by information overload, for which personalization is one of the treatments which... Sample PDF
Ontology-Based Personalization of E-Government Services
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Chapter 9
Maria Golemati, Costas Vassilakis, Akrivi Katifori, George Lepouras, Constantin Halatsis
Novel and intelligent visualization methods are being developed in order to accommodate user searching and browsing tasks, including new and... Sample PDF
Context and Adaptivity-Driven Visualization Method Selection
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Chapter 10
Honghua Dai
Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional... Sample PDF
Integrating Semantic Knowledge with Web Usage Mining for Personalization
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Chapter 11
Constantinos Mourlas
One way to implement adaptive software is to allocate resources dynamically during run-time rather than statically at design time. Design of... Sample PDF
Adaptive Presentation and Scheduling of Media Streams on Parallel Storage Servers
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Chapter 12
Gheorghita Ghinea
This study investigated two dimensions of cognitive style, including Verbalizer/Imager and Field Dependent/ Field Independent and their influence on... Sample PDF
Impact of Cognitive Style on User Perception of Dynamic Video Content
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Chapter 13
Mathias Bauer, Alexander Kröner, Michael Schneider, Nathalie Basselin
Limitation of the human memory is a well-known issue that anybody has experienced. This chapter discusses typical components and processes involved... Sample PDF
Building Digital Memories for Augmented Cognition and Situated Support
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Chapter 14
Rafael Morales, Nicolas Van Labeke, Paul Brna, María Elena Chan
It is believed that, with the help of suitable technology, learners and systems can cooperate in building a sufficiently accurate learner model they... Sample PDF
Open Learner Modelling as the Keystone of the Next Generation of Adaptive Learning Environments
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Chapter 15
Klaus Jantke, Christoph Igel, Roberta Sturm
Humans need assistance in learning. This is particularly true when learning is supported by modern information and communication technologies. Most... Sample PDF
From E-Learning Tools to Assistants by Learner Modelling and Adaptive Behavior
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Chapter 16
Violeta Damjanovic, Milos Kravcik
The process of training and learning in Web-based and ubiquitous environments brings a new sense of adaptation. With the development of more... Sample PDF
Using Emotional Intelligence in Personalized Adaptation
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Chapter 17
Yang Wang
This chapter presents a first-of-its-kind survey that systematically analyzes existing privacy-enhanced personalization (PEP) solutions and their... Sample PDF
Technical Solutions for Privacy- Enhanced Personalization
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About the Contributors