D. E. Denning. Secure statistical databases with random sample queries. ACM Trans. Database Syst., 5(3):291--315, 1980. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
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....of protecting against learning data items from aggregates, we are protecting against learning aggregates from individual items. Although the basic problem is quite different, as we move toward non random samples the two areas may overlap. Of particular note is work on random sampling queries [Denning, 1980]; this may provide tools to implement policies governing the creation of non random samples. Another possible starting point for this is artificialintelligence work on selection of training data [Cohen et al. 1995, Yang and Honavar, 1998] Preventing the adversary from selecting a good set of ....
Denning, D. E. (1980). Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3):291-- 315. 14
Cardinality-based Inference Control in Data Cubes - Wang, Wijesekera, Jajodia (Correct)
....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 intervals enclosing exact values [25] The inference control methods proposed for statistical ....
D.E. Denning. Secure statistical databases with random sample queries. ACM Trans. on Database Systems, 5(3):291--315, 1980.
An Agent-based Approach to Inference Prevention in.. - Chang, Moskowitz, Tracy (2002) (Correct)
....to only certain users. As in a centralized database system, it is often possible for a user to infer sensitive information from publicly available information by exploiting probability dependency relationships. We refer to a compromise of the confidentiality of sensitive data in this way [De80][Hi97] T98] as database inference. Distributed databases present challenges to inference prevention methods that are not present in centralized schemes [PCS00] CM02] This is because each database in a distributed system does not contain all the data that is necessary to learn the gamut of the ....
Denning, D. (1980) "Secure Statistical Database with Random Sample Queries," ACM Transaction on Database Systems, 5(3), pp. 291-315.
A Study Of Inference Problems In Distributed Databases - Liwu Chang Ira (2002) (Correct)
....data which is related to sensitive data must be examined and perhaps modified, thus requiring further data sanitization. The problem of preventing database inference in a stand alone database is quite challenging and has recently been under intensive study from diverse aspects (e.g. 2] 3] 5][6][7] 8] 9] 11] 13] 15] 16] 17] 18] 19] 29] 30] Database inference in distributed databases is an area in which very little work has been done. In [11] the authors showed that sensitive information can be revealed if users link information from several databases in a deterministic manner. ....
Denning, D. (1980) "Secure Statistical Database with Random Sample Queries," ACM Transaction on Database Systems, 5(3), pp. 291-315.
Cardinality-based Inference Control in Sum-only Data Cubes - Wang, Wijesekera, Jajodia (2002) (Correct)
....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 intervals enclosing exact values [24] The inference control methods proposed for statistical ....
D.E. Denning. Secure statistical databases with random sample queries. ACM Trans. on Database Systems, 5(3):291--315, 1980.
Hippocratic Databases - Agrawal, Kiernan, Srikant, Xu (2002) (12 citations) (Correct)
.... [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 queries that Hippocratic databases have to ....
D. Denning. Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3):291--315, Sept. 1980.
Random Sampling from Databases - Olken (1993) (37 citations) (Correct)
....also envision the application of their methods to real time applications [HOT89] Sampling may also be used to estimate the database parameters used by the query optimizer in choosing query evaluation plans. This is discussed more fully later in Chapter 2. Finally, sampling has been proposed [Den80] as a means of providing security for individual data, while permitting access to statistical aggregates. 1.5.3 Why don t DBMSs support sampling Given the variety of uses for sampling from databases, one might well wonder why facilities for random sampling queries have not yet been included in ....
....U.S. Census Bureau and other statistical database generators. It is also an increasingly important issue with statistical databases of purchasing behavior which are used for marketing analyses [Lar92] For a recent survey article on the topic see Guthrie [Gea89] The discussion below is based on [Den80, Gea89] which contain extensive bibliographies. A variety of methods of disclosure control have been attempted, but most have succumbed to various sophisticated attacks. Such attacks are often facilitated by the provision of sophisticated query facilities in modern statistical databases. If ....
[Article contains additional citation context not shown here]
Dorothy E. Denning. Secure Statistical Databases with Random Sample Queries. ACM Transactions on Database Systems, 5(3):291--315, Sept. 1980.
Using Sample Size to Limit Exposure to Data Mining - Clifton (2000) (7 citations) (Correct)
....of protecting against learning data items from aggregates, we are protecting against learning aggregates from individual items. Although the basic problem is quite di#erent, as we move toward non random samples the two areas may overlap. Of particular note is work on random sampling queries [Den80] this may provide tools to implement policies governing the creation of non random samples. Another possible starting point for this is artificial intelligence work on selection of training data [CKB95, YH98] Preventing the adversary from selecting a good set of training data (while still ....
Dorothy E. Denning. Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3):291--315, September 1980.
Privacy-Preserving Data Mining - Agrawal, Srikant (2000) (98 citations) (Correct)
.... 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 statistical quality is measured in terms of bias, ....
D.E. Denning. Secure statistical databases with random sample queries. ACM TODS, 5(3):291-- 315, Sept. 1980.
On Resampling for Statistical Confidentiality in.. - Domingo-Ferrer.. (1999) (Correct)
....For a more detailed description of the existing disclosure control methods and their evaluation criteria see [4] 5] 6] In [2] resampling was shown to be a principle generating a subclass of output perturbation methods for disclosure control. Specifically, Denning s random sample method [7] was extended and the bootstrap and the jack knife resampling techniques were also used. Using resampling methods is attractive because they are well characterised from a statistical point of view. This allows a pretty straightforward evaluation of their security properties. In [8] a practical ....
D. E. Denning, Secure statistical database with random sample queries. ACM Transactions on Database Systems, 5(3) 291-315 (1980).
Random Sampling from Databases - A Survey - Olken, Rotem (1994) (10 citations) (Correct)
....and other statistical database generators. It is also an increasingly important issue with statistical databases of purchasing behavior which are used for marketing analyses, see Larson (1992) For a recent survey article on the topic see Guthrie et al. 1989) The discussion below is based on Denning (1980) Guthrie et al. 1989) which contain extensive bibliographies. A variety of methods of disclosure control have been attempted, but most have succumbed to various sophisticated attacks. Such attacks are often facilitated by the provision of sophisticated query facilities in modern statistical ....
....The padding introduced by the trackers can be removed, to reveal the desired counts and individual data. Such trackers could, in principle, be defeated by refusing to process queries whose intersections (with previous queries) were too small. However, no practical method is known to do this (see Denning (1980)) Attention has thus focused on methods which avoid providing precisely correct answers to aggregate (COUNT, SUM, STD. DEV. queries. Such methods include: rounding the query results, perturbing the query results with additive, or multiplicative noise, perturbing the data values with additive or ....
[Article contains additional citation context not shown here]
Denning, D. E. (1980). Secure statistical databases with random sample queries, ACM Transactions on Database Systems 5, 291--315.
Random Sampling from Databases - Olken (1993) (37 citations) (Correct)
....They also envision the application of their methods to real time applications [HOT89] Sampling may also be used to estimate the database parameters used by the query optimizer in choosing query evaluation plans. This is discussed more fully later in Chapter 2. Finally, sampling has been proposed [Den80] as a means of providing security for individual data, while permitting access to statistical aggregates. CHAPTER 1. INTRODUCTION 7 1.5.3 Why don t DBMSs support sampling Given the variety of uses for sampling from databases, one might well wonder why facilities for random sampling queries ....
....statistical database generators. It is also an increasingly important issue with statistical databases of purchasing behavior CHAPTER 1. INTRODUCTION 17 which are used for marketing analyses [Lar92] For a recent survey article on the topic see Guthrie [Gea89] The discussion below is based on [Den80, Gea89] which contain extensive bibliographies. A variety of methods of disclosure control have been attempted, but most have succumbed to various sophisticated attacks. Such attacks are often facilitated by the provision of sophisticated query facilities in modern statistical databases. If users ....
[Article contains additional citation context not shown here]
Dorothy E. Denning. Secure Statistical Databases with Random Sample Queries. ACM Transactions on Database Systems, 5(3):291--315, Sept. 1980.
General Purpose Database Summarization - Saint-Paul, Raschia, Mouaddib (2005) (Correct)
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D. E. Denning. Secure statistical databases with random sample queries. ACM Trans. Database Syst., 5(3):291--315, 1980.
Universitat de Barcelona - Departament Estadstica Contribucions (Correct)
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D. E. Denning, "Secure statistical database with random sample queries", ACM Transactions on Database Systems, vol. 5(3), pp. 291-315, 1980.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
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D. E. Denning. Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3), 1980.
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (2004) (Correct)
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D. Denning. Secure statistical databases with random sample queries. ACM TODS, 5(3), 1980.
Privacy: A Machine Learning View - Vinterbo (2002) (Correct)
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Dorothy E. Denning, "Secure statistical databases with random sample queries," ACM Transactions on Database Systems, vol. 5, no. 3, pp. 291--315, 1980.
Privacy-Preserving Data Mining - How do we mine data when we.. - Clifton (Correct)
No context found.
D. E. Denning, "Secure statistical databases with random sample queries," ACM Transactions on Database Systems, vol. 5, no. 3, pp. 291--315, Sept. 1980.
Vision Paper: Enabling Privacy for the Paranoids - Aggarwal Bawa Ganesan (Correct)
No context found.
D. Denning. Secure statistical databases with random sample queries. ACM TODS, 5(3), 1980.
A Customizable k-Anonymity Model for Protecting Location Privacy - Gedik, Liu (2004) (Correct)
No context found.
D. E. Denning. Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3), 1980.
Privacy, Security, and Data Mining - How do we mine data when we.. - Clifton (2002) (Correct)
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Dorothy E. Denning. Secure statistical databases with random sample queries. ACM Transactions on Database Systems, 5(3):291--315, September 1980.
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
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D.E. Denning. Secure statistical databases with random sample queries. ACM Trans. on Database Systems, 5(3):291-315, 1980. 554
Auditing Interval-Based Inference - Li, Wang, Wang, Jajodia (2001) (Correct)
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D.E. Denning. Secure statistical databases with random sample queries. ACM Trans. on Database Systems, 5(3):291-315, 1980.
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