k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
IEEE Security and Privacy
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Controlling inference: avoiding p-level reduction during analysis
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
A Framework to Balance Privacy and Data Usability Using Data Degradation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk
Transactions on Data Privacy
Analysis of social networking privacy policies
Proceedings of the 2010 EDBT/ICDT Workshops
Privacy-enhanced web personalization
The adaptive web
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
New Approach to Quantification of Privacy on Social Network Sites
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Towards defining semantic foundations for purpose-based privacy policies
Proceedings of the first ACM conference on Data and application security and privacy
Capturing P3P semantics using an enforceable lattice-based structure
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
Ask a better question, get a better answer a new approach to private data analysis
ICDT'07 Proceedings of the 11th international conference on Database Theory
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
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Understanding privacy in a data storage environment has become of increasing interest to the data management and user communities over the past decade. Previous work has produced a number of definitions with greater or lesser specificity. The value of a particular definition can only be understood in light of how it helps us understand when a privacy violation occurs. This paper builds upon earlier work that defines privacy using a four-dimensional taxonomy with an inherent sense of increasing privacy exposure. This taxonomy is extended to formally capture the notions of (a) privacy violations, (b) the severity of a privacy violation, and (c) the likelihood of data providers ceasing to provide data due to privacy exposures. The privacy violation model developed here provides an operational framework to characterize and estimate privacy violation in a relational database system. It also allows one to calculate the consequences to the data provider of widening privacy policies. We describe a quantitative analysis of violations that captures discrepancies between the data collector's stated policies and practices in comparison to the data providers' data preferences. We demonstrate this analysis using a simple example and show how the accumulation of privacy violations can have a detrimental effect upon the data collector.