A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining
IEEE Transactions on Knowledge and Data Engineering
Secure and useful data sharing
Decision Support Systems
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Statistical confidentiality: Optimization techniques to protect tables
Computers and Operations Research
On-line data protecting via pseudo random binary sequences
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Privacy-preserving similarity-based text retrieval
ACM Transactions on Internet Technology (TOIT)
A three-dimensional conceptual framework for database privacy
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Using the jackknife method to produce safe plots of microdata
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
Pricing and disseminating customer data with privacy awareness
Decision Support Systems
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A practical method is presented for giving unlimited, deterministically correct, numerical responses to ad-hoc queries to an online database, while not compromising confidential numerical data. The method is appropriate for any size database, and no assumptions are needed about the statistical distribution of the confidential data. Responses are in the form of a number plus a guarantee, so the user can determine an interval that is sure to contain the exact answer. Virtually any imaginable query type can be answered, and in the absence of insider information, collusion among the users presents no problem. Experimental analysis supports the practical viability of the proposed method.