Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Using approximations to scale exploratory data analysis in datacubes
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
ACM Transactions on Database Systems (TODS)
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
Secure statistical databases with random sample queries
ACM Transactions on Database Systems (TODS)
A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
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
ACM Computing Surveys (CSUR)
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Auditing Interval-Based Inference
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Computational Issues Connected with the Protection of Sensitive Statistics by Auditing Sum Queries
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Constraints, Inference Channels and Secure Databases
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Privacy preserving mining of association rules
Proceedings of the eighth 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
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
Cardinality-based inference control in OLAP systems: an information theoretic approach
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Cardinality-based inference control in data cubes
Journal of Computer Security
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
Evaluating privacy threats in released database views by symmetric indistinguishability
Journal of Computer Security - Selected papers from the Third and Fourth Secure Data Management (SDM) workshops
Self-enforcing Private Inference Control
ProvSec '09 Proceedings of the 3rd International Conference on Provable Security
A cubic-wise balance approach for privacy preservation in data cubes
Information Sciences: an International Journal
New paradigm of inference control with trusted computing
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
FMC: an approach for privacy preserving OLAP
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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This paper addresses the inference problems in data warehouses and decision support systems such as on-line analytical processing (OLAP) systems. Even though OLAP systems restrict user accesses to predefined aggregations, inappropriate disclosure of sensitive attribute values may still occur. Based on a definition of non-compromiseability to mean that any member of a set of variables satisfying a given set of their aggregations can have more than one value, we derive sufficient conditions for non-compromiseability in sum-only data cubes. Under this definition, (1) the non-compromiseability of multi-dimensional aggregations can be reduced to that of one dimensional aggregations, (2) full or dense core cuboids are non-compromiseable, and (3) there is a tight lower bound for the cardinality of a core cuboid to remain non-compromiseable. Based on these results, taken together with a three-tier model for controlling inferences, we provide a divide-and-conquer algorithm that uniformly divides data sets into chunks and builds a data cube on each such chunk. The union of these data cubes are then used to provide users with inference-free OLAP queries.