k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Pseudo-random number generation for sketch-based estimations
ACM Transactions on Database Systems (TODS)
A Critique of k-Anonymity and Some of Its Enhancements
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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We consider the problem of developing a user-centric toolkit for anonymizing medical data that uses ε-differential privacy to measure disclosure risk Our work will use a randomized algorithm, in particular, the application of sketches to achieve differential privacy Sketch based randomization is a form of multiplicative perturbation that has been proven to work effectively on sparse, high dimensional data However, a differential privacy model has yet to be defined in order to work with sketches The goal is to study whether this approach will yield any improvement over previous results in preserving the privacy of data How much the anonymized data utility is retained will subsequently be evaluated by the usefulness of the published synthetic data for a number of common statistical learning algorithms.