A. Hundepool and L. Willenborg. - and # -Argus: Software for statistical disclosure control. In Third International Seminar on Statistical Confidentiality, Bled, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of data are being collected on individuals and entities. This is being fueled by progress in various technologies like storage, networking and automation in various business processes. Of particular interest are data containing structured information on individuals (referred to as micro data in [11, 10, 14]) Such data are Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. ....

....token could replace the uniquely identifying social security number of a person in U.S.A. However, it has been pointed out that this is not su#cient since the released data contains other information which when linked with other data sets can identify or narrow down the individuals or entities [10, 15, 17, 14]. An example in [14] illustrates the identification by linking a medical data set and a voter list using fields like zip code, date of birth and gender. In addition to the identity disclosure problem discussed above, attribute disclosure occurs when something about an individual is learnt from ....

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A. Hundepool and L. Willenborg. -and#-argus: Software for statistical disclosure control. In Proceedings of Third Internation Seminar on Statistical Confidentiality, 1996.

....of computing power because more data and more powerful tools are available for unwanted linking. The European Union in response to these growing concerns has recently funded a tremendous effort to develop solutions. Their first computational result was Argus from Statistics Netherlands [29]. I will examine this system in chapter 5 and show the first release of Argus does not provide adequate protection. 3.2 Multi level databases Another related area is aggregation and inference in multi level databases [30, 31, 32, 33, 34, 35] which concerns restricting the release of lower ....

A. Hundepool and L. Willenborg. - and t-argus: software for statistical disclosure control. Third International Seminar on Statistical Confidentiality. Bled: 1996.

....computing power since more data and more powerful tools are available for unwanted probabilistic linking. The European Union in response to these growing concerns has recently funded a tremendous effort to develop solutions. Their first computational result was P Argus from Statistics Netherlands [16]. We will examine this system later, but many tests have been conducted [17] that show the first release of P Argus does not provide adequate protection. 3.2 Multi level databases Another related area is aggregation and inference in multi level databases [18, 19, 20, 21, 22, 23] which concerns ....

....indirect disclosure if there are less than k such entities in P and that is a known fact. The requirement for k possible entities does not have to necessarily be maintained by R. K map disclosure is the most popular for non statistical databases. It is the protection scheme used by both P Argus [16] and Datafly [27] which are the only complete disclosure systems operational at the present time. Both systems generalize (or globally re code) values and suppress information in order to enforce k in the released information itself. This notion of enforcing k on the released data is termed k ....

A. Hundepool and L. Willenborg. P- and W- argus: software for statistical disclosure control. Third International Seminar on Statistical Confidentiality. Bled: 1996.

....are reconstructable. There is thus a problem and recipient specific limit on the effort one should put into hampering reconstruction. 4 Related Work The presented approach to anonymization is based on mathematical logic and indiscernibility. Complementary techniques often have statistical roots [3, 4]. Fienberg [3] provides an overview of statistical disclosure control and some of its issues. 4.1 Main Approaches Basically, there are three main approaches to making a database more anonymous: ffl Outlier removal. Certain rows or columns objects and or attributes are removed altogether. ffl ....

....and does not blur the perceived values of the non suppressed entries. The algorithms we present in this article are examples of cell suppression. 4.2 Software Systems Software systems for making confidential databases more anonymous exist. Sweeney [9] reviews how the Datafly [8, 9] and ARGUS [4] systems operate and perform. Neither Datafly nor ARGUS address relative anonymization. The Datafly system performs generalization and may remove outliers entirely, but does not incorporate cell suppression. The ARGUS system also performs generalization, but performs cell suppression instead ....

A. Hundepool, L. Willenborg (1996), ¯- and ø - ARGUS: Software for Statistical Disclosure Control, Third International Seminar on Statistical Confidentiality, Bled. Available from www.cbs.nl/sdc/.

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A. Hundepool and L. Willenborg. - and # -Argus: Software for statistical disclosure control. In Third International Seminar on Statistical Confidentiality, Bled, 1996.

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A. Hundepool and L. Willenborg. - and -Argus: Software for statistical disclosure control. In Third International Seminar on Statistical Con dentiality, Bled, MA (USA), 1996.

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