J. Traub, Y. Yemini, H. Wozniakowksi "The Statistical Security of a Statistical Database," ACM TODS, 9, 4 pp. 672--679, 1984. 11  Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Precisely Answering Multi-dimensional Range.. - Wang, Li.. (2003) (Correct)
.... query sets, detecting inferences by auditing all queries asked by a specific user [12, 10, 26, 6] suppressing sensitive data in released statistical tables [13] grouping tuples and treating each group as a single tuple [11, 32] Perturbation based techniques add noise to source data or outputs [35, 5, 34]. Other aspects of inference problem include the inference caused by arithmetic constraints [8, 7] inferring approximate values instead of exact values [30] and inferring intervals enclosing exact values [28, 27, 29] The inference control methods proposed for statistical databases do not ....
J.F. Traub, Y. Yemini, and H. Wo zniakowski. The statistical security of a statistical database. ACM Trans. on Database Systems, 9(4):672--679, 1984.
Cardinality-based Inference Control in Data Cubes - Wang, Wijesekera, Jajodia (Correct)
....partitioning data into mutually exclusive partitions [10, 11] while In the settings of this paper, each variable can have either one value or infinitely many values. restricting each query set to range over at most one partition. Perturbation based technique includes adding noise to source data [32], outputs [5, 28] database structure [30] or size of query sets (by sampling data to answer queries) 14] Some variations of the inference problem have been studied lately, such as the inference caused by arithmetic constraints [8, 7] inferring approximate values instead of exact values [27] ....
J.F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Trans. on Database Systems, 9(4):672--679, 1984.
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
.... some un divided data chunks [6,7] and (closer to our concerns in this paper) auditing all queries in order to determine whether inference is possible [8,5,20,23] For controlling statistical inference, some perturbation based techniques have been studied which include adding noise to source data [27,28], changing output resuits [2] altering database structure [25] or sampling data to answer queriesIll] Auditing We study the auditing approach in this paper. By auditing, all queries made by each user are logged and checked for possible inference before the results of new queries are released. ....
J.F. Traub, . emini, and H. Wonaikowski. The statistical security of a statistical database. ACM 7'ans. on Database Systems, 9(4):672-679, 1984. 554 568 Yingjiu Liet al.
Preventing Interval-based Inference by Random Data Perturbation - Li, Wang, Jajodia (2002) (Correct)
....enough to the unperturbed ones, leading to interval based inference. Random data perturbation (RDP) methods (i.e. adding random noise to senserive data) have been studied for many years, especially in the statistical database literature, to prevent both exact inference and statistical inference [12, 1, 11, 7, 10, 8]. For numerical attributes, most RDP methods generate noise based on Gaussian distribution [7, 10, 8] Such method is not effective to prevent interval based inference since arbitrarily small noise could be generated. To illustrate this, assume that a random noise from Gaussian distribution with ....
....inference, whereas this probability is zero for the RDP based on e Gaussian distribution. Discussion Formula (5) indicates that the error estimate to the query response is in a statistical sense. If database users are not satisfied with this, a correction scheme is to be developed. Traub et al. [11] proposed the following remedy scheme: i) monitor the error in each query and check whether it exceeds a predetermined threshold; ii) if the error threshold is exceeded and if the query size is large enough, simply perturbs the query response by adding some random noise until the error of the ....
[Article contains additional citation context not shown here]
J.F. Traub, Y. Yemini, and H. Wolniakowski. The statistical security of a statistical database. ACM Trans. on Database Systems, 9(4):672-679, 1984.
Cardinality-based Inference Control in Sum-only Data Cubes - Wang, Wijesekera, Jajodia (2002) (Correct)
....are possible [11, 8, 23, 25] suppressing sensitive data in a released statistical tables [12] partitioning data into mutually exclusive partition [9, 10] and restricting each query set to range over at most one partition. Perturbation based technique includes adding noise to source data [30], outputs [5, 26] database structure [28] or size of query sets (by sampling data to answer queries) 13] Some variations of the inference problem have been studied lately, such as the inference caused by arithmetic constraints [7, 6] inferring approximate values instead of exact values [25] ....
J.F. Traub, Y. Yemini, and H. Wo zniakowski. The statistical security of a statistical database. ACM Trans. on Database Systems, 9(4):672--679, 1984.
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
.... undivided data chunks [6, 7] and (closer to our concerns in this paper) auditing all queries in order to determine whether inference is possible [8, 5, 20, 23] For controlling statistical inference, some perturbation based techniques have been studied which include adding noise to source data [27, 28], changing output results [2] altering database structure [25] or sampling data to answer queries[ill. Auditing We study the auditing approach in this paper. By auditing, all queries made by each user are logged and checked for possible inference before the results of new queries are released. ....
J.F. Traub, Y. Yemini, and H. Wonaikowski. The statistical security of a statistical database. ACM cans. on Database Systems, 9(4):672-679, 1984.
Hippocratic Databases - Agrawal, Kiernan, Srikant, Xu (2002) (12 citations) (Correct)
.... 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] Hippocratic databases share with statistical databases the goal of preventing disclosure of private information, and hence some of the techniques developed for statistical databases will find application ....
J. Traub, Y. Yemini, and H. Woznaikowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9(4):672--679, Dec. 1984.
Auditing Boolean Attributes - Kleinberg, Papadimitriou, Raghavan (2000) (5 citations) (Correct)
....of the salary information This is the classical statistical database security problem, studied extensively since the 1970 s; see [1] for a survey. The main approaches to this problem involve perturbing the data so as to maintain their statistical characteristics but prevent their compromise [13, 11, 16], to perturb the responses for the same purpose, 2, 8] to restrict the size or overlap of the statistical queries [10, 9] or, finally (and closer to our concerns here) to audit the # Department of Computer Science, Cornell University, Ithaca NY 14853. Email: kleinber cs.cornell.edu. Supported ....
J. Traub, Y. Yemini, H. Wozniakowksi "The Statistical Security of a Statistical Database," ACM TODS, 9, 4 pp. 672--679, 1984. 11
Privacy-Preserving Data Mining - Agrawal, Srikant (2000) (98 citations) (Correct)
.... 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 (e.g. Bec80] and sampling the result of a query (e.g. Den80] There are negative results showing that the proposed techniques cannot satisfy the conflicting objectives of providing high quality statistics and at the same time prevent exact or ....
J.F. Traub, Y. Yemini, and H. Woznaikowski. The statistical security of a statistical database. ACM TODS, 9(4):672--679, Dec. 1984.
Auditing Boolean Attributes - Jon Kleinberg Christos (Correct)
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J. Traub, Y. Yemini, H. Wozniakowksi "The Statistical Security of a Statistical Database," ACM TODS, 9, 4 pp. 672--679, 1984. 11
Microdata Disclosure Risk in Database Privacy Protection - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Perturbative Masking for Microdata Privacy Protection in.. - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Non-Perturbative Methods for Microdata Privacy in Statistical.. - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Microaggregation for Privacy Protection in Statistical Databases - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Information Loss Measures for Microdata in Database Privacy.. - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984. 12
Additive Noise for Microdata Privacy Protection in Statistical.. - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Trading Off Information Loss and Disclosure Risk in Database.. - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
A Privacy-Preserving Index for Range Queries - Hore, Mehrotra, Tsudik (2004) (5 citations) (Correct)
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Traub, J., F., Yemini, T., and Wozniakowski, H. The Statistical Security of a Statistical Database. TODS 1984, 672-679.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9(4), 1984. 12
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (2004) (Correct)
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J. Traub, Y. Yemini, and H. Woznaikowski. The statistical security of a statistical database. ACM TODS, 9(4), 1984.
Synthetic Microdata Generation for Database Privacy Protection - Crises (2004) (Correct)
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J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9:672--679, 1984.
Private Inference Control - Woodruff, Staddon (2004) (1 citation) (Correct)
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J. Traub, Y. Yemini and H Wozniakowski. The statistical security of a statistical database. In ACM TODS, pp.672-679, 1984.
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (Correct)
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J. Traub, Y. Yemini, and H. Woznaikowski. The statistical security of a statistical database. ACM TODS, 9(4), 1984.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
No context found.
J. F. Traub, Y. Yemini, and H. Wozniakowski. The statistical security of a statistical database. ACM Transactions on Database Systems, 9(4), 1984. 12
Survey of Techniques for Securing Statistical Databases - Jason Schatz University (Correct)
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) J. F. Truab and Y. Yemini. The Statistical Security of A Statistical Database. ACM transactions on Database Systems, Vol. 9, No. 4, December 1984, Pages
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