ACM Transactions on Database Systems (TODS)
The tracker: a threat to statistical database security
ACM Transactions on Database Systems (TODS)
Security in statistical databases for queries with small counts
ACM Transactions on Database Systems (TODS)
A model of statistical database their 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)
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
Security in Databases: A Combinatorial Study
Journal of the ACM (JACM)
A study on the protection of statistical data bases
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
Efficient inference control for range SUM queries on statistical data bases
SSDBM'81 Proceedings of the 1st LBL Workshop on Statistical database management
Journal of the ACM (JACM)
Multidimensional databases
Privacy in multidimensional databases
Multidimensional databases
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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)
Towards robustness in query auditing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
An analytical approach to the inference of summary data of additive type
Theoretical Computer Science
Information disclosure under realistic assumptions: privacy versus optimality
Proceedings of the 14th ACM conference on Computer and communications security
Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
Computational complexity of auditing finite attributes in statistical databases
Journal of Computer and System Sciences
A Bayesian approach for on-line max and min auditing
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Guided perturbation: towards private and accurate mining
The VLDB Journal — The International Journal on Very Large Data Bases
Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure
Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security
Simulatable Binding: Beyond Simulatable Auditing
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Output perturbation with query relaxation
Proceedings of the VLDB Endowment
Secure aggregation in a publish-subscribe system
Proceedings of the 7th ACM workshop on Privacy in the electronic society
An efficient online auditing approach to limit private data disclosure
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Bayesian model for disclosure control in statistical databases
Data & Knowledge Engineering
Self-enforcing Private Inference Control
ProvSec '09 Proceedings of the 3rd International Conference on Provable Security
A Bayesian approach for on-line max auditing of dynamic statistical databases
Proceedings of the 2009 EDBT/ICDT Workshops
Restoring compromised privacy in micro-data disclosure
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Memory organization schemes for large shared data
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
New paradigm of inference control with trusted computing
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Protecting data privacy through hard-to-reverse negative databases
ISC'06 Proceedings of the 9th international conference on Information Security
A bayesian approach for on-line sum/count/max/min auditing on boolean data
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Denials leak information: Simulatable auditing
Journal of Computer and System Sciences
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The basic inference problem is defined as follows: For a finite set X = {xi, 聟 , xn}, we wish to infer properties of elements of X on the basis of sets of "queries" regarding subsets of X. By restricting these queries to statistical queries, the statistical database (SDB) security problem is obtained. The security problem for the SDB is to limit the use of the SDB so that only statistical information is available and no sequence of queries is sufficient to infer protected information about any individual. When such information is obtained the SDB is said to be compromised. In this paper, two applications concerning the security of the SDB are considered: On-line application. The queries are answered one by one in sequence and it is necessary to determine whether the SDB is compromised if a new query is answered. Off-line application. All queries are available at the same time and it is necessary to determine the maximum subset of queries to be answered without compromising the SDB. The complexity of these two applications, when the set of queries consists of (a) a single type of SUM query, (b) a single type of MAX/MIN query, (c) mixed types of MAX and MIN queries, (d) mixed types of SUM and MAX/MIN queries, and (e) mixed types of SUM, MAX, and MIN queries, is studied. Efficient algorithms are designed for some of these situations while others are shown to be NP-hard.