By Sarah Giessing (auth.), Josep Domingo-Ferrer, Vicenç Torra (eds.)
Privacy in statistical databases is set ?nding tradeo?s to the stress among the expanding societal and budget friendly call for for exact info and the criminal and moral legal responsibility to guard the privateness of people and companies, that are the resource of the statistical info. Statistical organizations can't anticipate to assemble actual info from person or company respondents except those believe the privateness in their responses is assured; additionally, fresh surveys of net clients convey majority of those are unwilling to supply information to an internet site until they understand that privateness safeguard measures are in position. “Privacy in Statistical Databases2004” (PSD2004) was once the ?nal convention of the CASC venture (“Computational points of Statistical Con?dentiality”, IST-2000-25069). PSD2004 is within the type of the subsequent meetings: “Stat- tical information Protection”, held in Lisbon in 1998 and with court cases released through the O?ce of O?cial courses of the EC, and likewise the AMRADS venture SDC Workshop, held in Luxemburg in 2001 and with lawsuits released via Springer-Verlag, as LNCS Vol. 2316. this system Committee authorized 29 papers out of forty four submissions from 15 di?erentcountriesonfourcontinents.Eachsubmittedpaperreceivedatleasttwo experiences. those court cases include the revised types of the approved papers. those papers hide the rules and techniques of tabular facts safety, overlaying equipment for the safety of person information (microdata), artificial facts new release, disclosure probability research, and software/case studies.
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Extra resources for Privacy in Statistical Databases: CASC Project Final Conference, PSD 2004, Barcelona, Spain, June 9-11, 2004. Proceedings
Research in Official Statistics, 5, 35–64. Dalenius, Tore (1977). Towards a methodology for statistical disclosure control. Statistisk Tidskrift, 5, 429–444. Dalenius, Tore (1988). Controlling Invasion of Privacy in Surveys. Statistics Sweden, Stockholm. Dalenius, Tore and Reiss, Steven P. (1978). Data-swapping: A technique for disclosure control (extended abstract). American Statistical Association, Proceedings of the Section on Survey Research Methods, Washington, DC, 191–194. Dalenius, Tore and Reiss, Steven P.
Org. Rather than aiming to preserve any specific set of statistics, the NISS procedure focuses on the trade-off between disclosure risk and data utility. Both risk and utility diminish as the number of swap variables and the swap rate increase. For example, a high swapping rate implies that data are well-protected from compromise, but also that their inferential properties are more likely to be distorted. Gomatam, Karr and Sanil (2004) formulate the problem of choosing optimal values for these parameters as a decision problem that can be viewed in terms of a risk-utility frontier.
In particular, suppose that X represents sensitive variables and S non-sensitive variables. , Y, provide an intruder with no additional information about One of the problems is, of course, that is unknown and thus there is information in Y. Replace the rank order values of Y with those of X, as in rank swapping. They provide some simulation results that they argue show the superiority of their method over rank swapping in terms of data protection with little or no loss in the ability to do proper inferences in some simple bivariate and trivariate settings.