Practical data-swapping: the first steps
ACM Transactions on Database Systems (TODS)
A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
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
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Caching multidimensional queries using chunks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An efficient processing of range-MIN/MAX queries over data cube
Information Sciences: an International Journal
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
The tracker: a threat to statistical database security
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)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Towards OLAP security design — survey and research issues
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
High Performance OLAP and Data Mining on Parallel Computers
Data Mining and Knowledge Discovery
Recovering Information from Summary Data
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Cardinality-Based Inference Control in Sum-Only Data Cubes
ESORICS '02 Proceedings of the 7th European Symposium on Research in Computer Security
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Building Large ROLAP Data Cubes in Parallel
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Efficient approaches for materialized views selection in a data warehouse
Information Sciences: an International Journal
Data warehouse enhancement: A semantic cube model approach
Information Sciences: an International Journal
Privacy preserving data mining of sequential patterns for network traffic data
Information Sciences: an International Journal
Building a secure star schema in data warehouses by an extension of the relational package from CWM
Computer Standards & Interfaces
Information Sciences: an International Journal
Journal of Computer and System Sciences
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Hi-index | 0.07 |
A data warehouse stores current and historical records consolidated from multiple transactional systems. Securing data warehouses is of ever-increasing interest, especially considering areas where data are sold in pieces to third parties for data mining practices. In this case, existing data warehouse security techniques, such as data access control, may not be easy to enforce and can be ineffective. Instead, this paper proposes a data perturbation based approach, called the cubic-wise balance method, to provide privacy preserving range queries on data cubes in a data warehouse. This approach is motivated by the following observation: analysts are usually interested in summary data rather than individual data values. Indeed, our approach can provide a closely estimated summary data for range queries without providing access to actual individual data values. As demonstrated by our experimental results on APB benchmark data set from the OLAP council, the cubic-wise balance method can achieve both better privacy preservation and better range query accuracy than random data perturbation alternatives.