Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
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)
A study on the protection of statistical data bases
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Security problems on inference control for SUM, MAX, and MIN queries
Journal of the ACM (JACM)
Learning missing values from summary constraints
ACM SIGKDD Explorations Newsletter
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The inference control technique called Auditing is discussed in this paper. Auditing is in many ways better than the previously known techniques. Auditing would log all answered queries, and use this information to decide whether a new query could lead to compromise. Unfortunately, except for small SDB's, Auditing may not be readily usable in practice because of its excessive time and storage complexity in processing a new query. In this paper we restrict our study to SUM queries. Since it is unrealistic to assume that the user can obtain statistical information of any subset of the records in the SDB, we assume that statistical information is only available for those subsets of records in which one of their attribute values lies within a certain range (range query). With the proper data structure, the time and storage complexity for checking a new range query can be reduced to O(n) time and storage, or O(t log n) time with O(n2) storage for t new range queries and n records in the SDB