Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Machine Learning
Coding Theory and Cryptography: The Essentials
Coding Theory and Cryptography: The Essentials
Privacy in e-commerce: stated preferences vs. actual behavior
Communications of the ACM - Transforming China
Improving understanding of website privacy policies with fine-grained policy anchors
WWW '05 Proceedings of the 14th international conference on World Wide Web
Privacy practices of Internet users: self-reports versus observed behavior
International Journal of Human-Computer Studies - Special isssue: HCI research in privacy and security is critical now
Privacy and identity management for everyone
Proceedings of the 2005 workshop on Digital identity management
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
User interfaces for privacy agents
ACM Transactions on Computer-Human Interaction (TOCHI)
How Internet Users' Privacy Concerns Have Evolved since 2002
IEEE Security and Privacy
ARES '11 Proceedings of the 2011 Sixth International Conference on Availability, Reliability and Security
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Current approaches to privacy policy comparison use strict evaluation criteria (e.g. user preferences) and are unable to state how close a given policy is to fulfil these criteria. More flexible approaches for policy comparison is a prerequisite for a number of more advanced privacy services, e.g. improved privacy-enhanced search engines and automatic learning of privacy preferences. This paper describes the challenges related to policy comparison, and outlines what solutions are needed in order to meet these challenges in the context of preference learning privacy agents.