Yu, C., T., Chin, F., Y. A study in protection of statistical databases. SIGMOD 1977, pp. 169-181. Home/Search Document Not in Database Summary Related Articles Check |
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A Privacy-Enhanced Microaggregation Method - Li, Zhu, Wang, Jajodia (2002) (Correct)
....et al. the average over each group instead of individual records. A similar idea has also been discussed in statistical database literature where a database is partitioned into atomic populations (i.e. mutually exclusive groups) that constitute the raw materials available to the database users [3, 2, 13, 17]. Previous microaggregation methods require that the size of each partition group be a fixed constant k, called fixed size microaggregation [5, 4] Recent developments [6, 9] have concentrated on reducing information loss by allowing the size of each partition group to vary (with lower bound k) ....
Yu, C.T., Chin, F.Y.: A study on the protection of statistical databases. Proc. of ACM SIGMOD Int'l Conf. on Management of Data (1977) 169-181
Hippocratic Databases - Agrawal, Kiernan, Srikant, Xu (2002) (12 citations) (Correct)
.... size of query results [13] 18] controlling the overlap among successive queries [14] keeping audit trails of all answered queries and constantly checking for possible compromises [8] suppression of data cells of small size [9] and clustering entities into mutually exclusive atomic populations [61]. The perturbation family includes swapping values between records [12] replacing the original database by a sample from the same distribution [33] 42] adding noise to the values in the database [52] 57] adding noise to the results of a query [4] and sampling the result of a query [11] ....
C. Yu and F. Chin. A study on the protection of statistical databases. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 169--181, 1977.
Privacy-Preserving Data Mining - Agrawal, Srikant (2000) (98 citations) (Correct)
.... the overlap amongst successive queries (e.g. DJL79] keeping audit trail of all answered queries and constantly checking for possible compromise (e.g. CO82] suppression of data cells of small size (e.g. Cox80] and clustering entities into mutually exclusive atomic populations (e.g. YC77] The perturbation family includes swapping values between records (e.g. Den82] replacing the original database by a sample from the same distribution (e.g. LST83] LCL85] Rei84] adding noise to the values in the database (e.g. TYW84] War65] adding noise to the results of a query ....
C.T. Yu and F.Y. Chin. A study on the protection of statistical databases. In Proc. ACM SIGMOD Int. Conf. Management of Data, pages 169--181, 1977. 12
A Privacy-Preserving Index for Range Queries - Hore, Mehrotra, Tsudik (2004) (5 citations) (Correct)
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Yu, C., T., Chin, F., Y. A study in protection of statistical databases. SIGMOD 1977, pp. 169-181.
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