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)
Linear queries in statistical databases
ACM Transactions on Database Systems (TODS)
A fast procedure for finding a tracker in a statistical database
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)
A study on the protection of statistical data bases
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
Inference from statistical data bases.
Inference from statistical data bases.
Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
ACM Transactions on Database Systems (TODS)
Protecting statistical databases: a matter of privacy
ACM SIGCAS Computers and Society
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
Compromising statistical databases responding to queries about means
ACM Transactions on Database Systems (TODS)
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Statistical Relational Databases: Normal Forms
IEEE Transactions on Knowledge and Data Engineering
Multidimensional databases
Query directed partitioning scheme for securing statistical databases
SSDBM'81 Proceedings of the 1st LBL Workshop on Statistical database management
A security model for the statistical database problem
SSDBM'83 Proceedings of the 2nd international workshop on Proceedings of the Second International Workshop on Statistical Database Management
Statistical databases: their model, query language and security
SSDBM'83 Proceedings of the 2nd international workshop on Proceedings of the Second International Workshop on Statistical Database Management
Optimal distribution of restricted ranges in secure statistical database
SSDBM'1990 Proceedings of the 5th international conference on Statistical and Scientific Database Management
Research topics in statistical and scientific database management: the IV SSDBM
SSDBM'1988 Proceedings of the 4th international conference on Statistical and Scientific Database Management
Ranges and trackers in statistical databases
SSDBM'1988 Proceedings of the 4th international conference on Statistical and Scientific Database Management
On tracker attacks in health grids
Proceedings of the 2006 ACM symposium on Applied computing
Denormalization strategies for data retrieval from data warehouses
Decision Support Systems
An overview of computer security
IBM Systems Journal
Self-enforcing Private Inference Control
ProvSec '09 Proceedings of the 3rd International Conference on Provable Security
New paradigm of inference control with trusted computing
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
A three-dimensional conceptual framework for database privacy
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
| Hi-index | 0.00 |
Statistical evaluation of databases which contain personal records may entail risks for the confidentiality of the individual records. The risk has increased with the availability of flexible interactive evaluation programs which permit the use of trackers, the most dangerous class of snooping tools known. A class of trackers, called union trackers, is described. They permit reconstruction of the entire database without supplementary knowledge and include the general tracker recently described as a special case. For many real statistical databases the overwhelming majority of definable sets of records will form trackers. For such databases a random search for a tracker is likely to succeed rapidly. Individual trackers are redefined and counted and their cardinalities are investigated. If there are n records in the database, then most individual trackers employ innocent cardinalities near n/3, making them difficult to detect. Disclosure with trackers usually requires little effort per retrieved data element.