PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Anonymized data: generation, models, usage
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Maximizing Privacy under Data Distortion Constraints in Noise Perturbation Methods
Privacy, Security, and Trust in KDD
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
On wavelet decomposition of uncertain time series data sets
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Can the Utility of Anonymized Data be Used for Privacy Breaches?
ACM Transactions on Knowledge Discovery from Data (TKDD)
Similarity matching for uncertain time series: analytical and experimental comparison
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Uncertain time-series similarity: return to the basics
Proceedings of the VLDB Endowment
Information preservation in statistical privacy and bayesian estimation of unattributed histograms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
UV-diagram: a voronoi diagram for uncertain spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Information quality-aware tracking in uncertain sensor network
International Journal of Sensor Networks
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The problem of privacy-preserving data mining has been studied extensively in recent years because of the increased amount of personal information which is available to corporations and individuals. Most privacy transformations use some form of data perturbation or representational ambiguity in order to reduce the risk of identification. The final results from privacy transformation methods often require the underlying applications to be modified in order to work with the new representation of the data. Since the end results of privacy-transformation methods have not been standardized, the required modifications may vary with the method used for the privacy transformation. In some cases, it can be an enormous effort to re-design applications to work with the anonymized data. While the results of privacy-transformation methods are a natural form of uncertain data, the two problems have generally been studied independently. In this paper, we make a first attempt to unify the two fields, and propose a privacy transformation for which existing uncertain data management tools can be directly used. This is a great advantage, since it means that the wide spectrum of research available for uncertain data management can also be used for privacy-preserving data mining. We propose an uncertain version of the k-anonymity model which is related to the well known deterministic model of k-anonymity. The uncertain version of the k-anonymity model has the additional feature of introducing greater uncertainty for the adversary over an equivalent deterministic model.