Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Elements of information theory
Elements of information theory
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Selective private function evaluation with applications to private statistics
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Cardinality-Based Inference Control in Sum-Only Data Cubes
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Limiting privacy breaches in privacy preserving data mining
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
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
A Robust Sampling-Based Framework for Privacy Preserving OLAP
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Balancing accuracy and privacy of OLAP aggregations on data cubes
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Journal of Computer and System Sciences
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Computing join aggregates over private tables
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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We address the inference control problem in data cubes with some data known to users through external knowledge. The goal of inference controls is to prevent exact values of sensitive data from being inferred through answers to online analytical processing (OLAP) queries. We present an information theoretic approach for cardinality-based inference control, which simply counts the number of cells that all queries have covered thus far to determine whether a new query should be answered. Compared to previous approaches in sum-only data cubes, our new approach has a more general framework (applies to MIN, MAX and SUM) and is more effective.