Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
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
Caching multidimensional queries using chunks
SIGMOD '98 Proceedings of the 1998 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)
Cryptography and data security
Cryptography and data security
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
ICDT '03 Proceedings of the 9th International Conference on Database Theory
Auditing Interval-Based Inference
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Usability of Compromise-Free Statistical Databases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
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
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
Cell Suppression to Limit Content-Based Disclosure
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information System Track-Organizational Systems and Technology - Volume 3
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
Safety in automated trust negotiation
ACM Transactions on Information and System Security (TISSEC)
Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
A Robust Sampling-Based Framework for Privacy Preserving OLAP
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
A secure multiparty computation privacy preserving OLAP framework over distributed XML data
Proceedings of the 2010 ACM Symposium on Applied Computing
A systematic literature review of inference strategies
International Journal of Information and Computer Security
Balancing accuracy and privacy of OLAP aggregations on data cubes
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Surveillance, privacy and the law of requisite variety
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Journal of Computer and System Sciences
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
| Hi-index | 0.00 |
This paper addresses the inference problem in on-line analytical processing (OLAP) systems. The inference problem occurs when the exact values of sensitive attributes can be determined through answers to OLAP queries. Most existing inference control methods are computationally expensive for OLAP systems, because they ignore the special structures of OLAP queries. By exploiting such structures, we derive cardinality-based sufficient conditions for safe OLAP data cubes, Specifically, data cubes are safe, from inferences if their core cuboids are dense enough, in the sense that the number of known values is under a tight bound. We then apply the sufficient conditions on the basis of a three-tier inference control model. The model introduces an aggregation tier between data and queries. The aggregation tier represents a collection of safe data cubes that are pre-computed over a partition of the data using the proposed sufficient conditions. The aggregation tier is then used to provide users with inference-free queries. Our approach mitigates the performance penalty of inference control, because partitioning the data yields smaller input to inference control algorithms, pre-computing the aggregation tier reduces on-line delay, and using cardinality-based conditions guarantees linear-time complexity.