Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Role-Based Access Control Models
Computer
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
OLAP and statistical databases: similarities and differences
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
Secure databases: protection against user influence
ACM Transactions on Database Systems (TODS)
An authorization mechanism for a relational database system
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
Flexible support for multiple access control policies
ACM Transactions on Database Systems (TODS)
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Inference Control in Statistical Databases, From Theory to Practice
Inference Control in Statistical Databases, From Theory to Practice
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Cardinality-based inference control in OLAP systems: an information theoretic approach
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Cardinality-based inference control in data cubes
Journal of Computer Security
Overcoming Limitations of Approximate Query Answering in OLAP
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
Privacy preservation for data cubes
Knowledge and Information Systems
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
Answering top-k queries with multi-dimensional selections: the ranking cube approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
FMC: an approach for privacy preserving OLAP
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Journal of Computer and System Sciences
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Trustworthiness analysis of sensor data in cyber-physical systems
Journal of Computer and System Sciences
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A robust sampling-based framework for privacy preserving OLAP is introduced and experimentally assessed in this paper. The most distinctive characteristic of the proposed framework consists in adopting an innovative privacy OLAP notion, which deals with the problem of preserving the privacy of OLAP aggregations rather than the one of data cube cells, like in conventional perturbation-based privacy preserving OLAP techniques. This results in a greater theoretical soundness, and lower computational overheads due to processing massive-in-size data cubes. Also, the performance of our privacy preserving OLAP technique is compared with the one of the method Zero-Sum, the state-of-the-art privacy preserving OLAP perturbation-based technique, under several perspectives of analysis. The derived experimental results confirm to us the benefits deriving from adopting our proposed framework for the goal of preserving the privacy of OLAP data cubes.