L. Beck "A Security Mechanism for Statistical Databases," ACM TODS, 5, 3, pp. 316-- 338, 1980. 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 ....
L.L. Beck. A security mechanism for statistical databases. ACM Trans. on Database Systems, 5(3):316--338, 1980.
Cardinality-based Inference Control in Data Cubes - Wang, Wijesekera, Jajodia (Correct)
....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] and inferring ....
L.L. Beck. A security mechanism for statistical databases. ACM Trans. on Database Systems, 5(3):316--338, 1980.
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
.... 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. For auditing exact inference ....
.... one or zero, and each real variable xi is bounded: li xi ri, 1 i n, then the k th variable xk must fall into the following interval Ik: raax(lk, b airi) min(rk, b ik aili) if ak = 1 = r] if a = 0 For example, given x x2 = 5 and 1 x,x2 3, we have 2 x,x2 3 and I = I2 = [2, 3]. If the length of the interval Ik is less than a predetermined value (e.g. 5 of the actual value of xk) then variable xk can be considered as compromised in terms of interval based inference. This shows that even a single sum query of multiple variables may be vulnerable to interval based ....
L.L. Beck. A security mechanism for statistical databases. ACM Trans. on Database Systems, 5(3):316-338, 1980. 554
Cardinality-based Inference Control in Sum-only Data Cubes - Wang, Wijesekera, Jajodia (2002) (Correct)
....[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] and inferring ....
L.L. Beck. A security mechanism for statistical databases. ACM Trans. on Database Systems, 5(3):316--338, 1980.
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
.... (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. For auditing exact inference ....
.... and each real variable xi is bounded: li xi ri, i i n, then the k th variable xk must fall into the following interval Ik: max(l, b Zi airi) min(r, b Zi aili) if a = 1 I = l,r] if a = 0 For example, given x x= 5 and 1 x,x= 3, we have 2 x,x= 3 and I = I= [2, 3]. If the length of the interval Ik is less than a predetermined value (e.g. 5 of the actual value of x) then variable x can be considered as compromised in terms of interval based inference. This shows that even a single sum query of multiple variables may be vulnerable to interval based ....
L.L. Beck. A security mechanism for statistical databases. ACM Trans. on Database Systems, 5(3):316-338, 1980.
Hippocratic Databases - Agrawal, Kiernan, Srikant, Xu (2002) (12 citations) (Correct)
.... 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 in Hippocratic databases. However, the class of ....
L. L. Beck. A security mechanism for statistical databases. ACM Transactions on Database Systems, 5(3):316--338, September 1980.
Auditing Boolean Attributes - Kleinberg, Papadimitriou, Raghavan (2000) (5 citations) (Correct)
....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 in part by a David and Lucile Packard Foundation ....
L. Beck "A Security Mechanism for Statistical Databases," ACM TODS, 5, 3, pp. 316-- 338, 1980.
Privacy-Preserving Data Mining - Agrawal, Srikant (2000) (98 citations) (Correct)
.... 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 partial disclosure of individual information [AW89] The ....
Leland L. Beck. A security mechanism for statistical databases. ACM TODS, 5(3):316--338, September 1980.
Static Detection of Security Flaws in Object-Oriented Databases - Tajima (1996) (1 citation) (Correct)
....we show in this paper and the mechanisms proposed in those researches is also an interesting issue. In this paper, we do not discuss properties of aggregate functions on sets. Interesting studies on that topic has been shown in the context of statistical databases [KU77, Chi78, DDS79, DJL79, Bec80, C O82] The result of these researches say, in short, that aggregate functions on a set of data almost always reveal the information on the individual elements of the set. Acknowledgments I would like to thank Atsushi Ohori for his valuable suggestions and discussions through the research. ....
Leland L. Beck. A security mechanism for statistical databases. ACM TODS, 5(3):316--338, Sep. 1980.
Auditing Boolean Attributes - Jon Kleinberg Christos (Correct)
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L. Beck "A Security Mechanism for Statistical Databases," ACM TODS, 5, 3, pp. 316-- 338, 1980.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
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L. L. Beck. A security mechanism for statistical databases. ACM Transactions on Database Systems, 5(3), 1980.
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (2004) (Correct)
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I. I. Beck. A security mechanism for statistical databases. ACM TODS, 5(3), 1980.
Private Inference Control - Woodruff, Staddon (2004) (1 citation) (Correct)
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L. Beck. A security mechanism for statistical databases. In ACM TODS, pp. 316-338, 1980.
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (Correct)
No context found.
I. I. Beck. A security mechanism for statistical databases. ACM TODS, 5(3), 1980.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
No context found.
L. L. Beck. A security mechanism for statistical databases. ACM Transactions on Database Systems, 5(3), 1980.
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