SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
The knowledge complexity of interactive proof systems
SIAM Journal on Computing
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Quickly generating billion-record synthetic databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Automatic test data generation using constraint solving techniques
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting data privacy in private information retrieval schemes
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
A framework for testing database applications
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Web Implementation of a Securtty Mediator for Medical Databases
Proceedings of the IFIP TC11 WG11.3 Eleventh International Conference on Database Securty XI: Status and Prospects
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Protecting Inappropriate Release of Data from Realistic Databases
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A research agenda for distributed software development
Proceedings of the 28th international conference on Software engineering
Information and Software Technology
Privacy Preserving Database Generation for Database Application Testing
Fundamenta Informaticae - Special issue ISMIS'05
A case study in database reliability: component types, usage profiles, and testing
Proceedings of the 1st international workshop on Testing database systems
Statistical database modeling for privacy preserving database generation
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Privacy Preserving Database Generation for Database Application Testing
Fundamenta Informaticae - Special issue ISMIS'05
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Traditionally, application software developers carry out their tests on their own local development databases. However, such local databases usually have only a small number of sample data and hence cannot simulate satisfactorily a live environment, especially in terms of performance and scalability testing. On the other hand, the idea of testing applications over live production databases is increasingly problematic in most situations primarily due to the fact that such use of live production databases has the potential to expose sensitive data to an unauthorized tester and to incorrectly update information in the underlying database. In this paper, we investigate techniques to generate mock databases for application software testing without revealing any confidential information from the live production databases. Specifically, we will design mechanisms to create the deterministic rule set R, non-deterministic rule set N R, and statistic data set S for a live production database. We will then build a security Analyzer which will process the triplet together with security requirements (security policy) and output a new triplet The security Analyzer will guarantee that no confidential information could be inferred from the new triplet The mock database generated from this new triplet can simulate the live environment for testing purpose, while maintaining the privacy of data in the original database.