Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Theoretical computer science wikipedia , lookup
Pattern recognition wikipedia , lookup
Computational electromagnetics wikipedia , lookup
Neuroinformatics wikipedia , lookup
Corecursion wikipedia , lookup
Data analysis wikipedia , lookup
Multidimensional empirical mode decomposition wikipedia , lookup
Summary of Topic ii (Tabular Data Protection) Frequency Tables Magnitude Tables Web Access © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Frequency Tables C. Dwork et al. (Microsoft Research) suggest a new concept for disclosure risk avoidance for frequency data - “differential privacy” – and techniques to ensure it. These techniques are based on adding noise to Fourier coefficients corresponding to a given contingency table. A new method for assessing disclosure riskother (i.e.SDC risk of attributeensure Limitations (No of variables/Categories)? Could methods disclosure) for tables of counts, the subtraction - attribution probability differential privacy? Applicable to tabulations from a survey with hundreds of (SAP) method has been proposed by D. Smith and M. Elliot (University of variables? Manchester). N. Shlomo (University of Southampton) compares the performance of several techniques to protect population counts tables with respect to disclosure risk and information loss. Statistics New Zealand uses Random Rounding, a mean cell size rule and a threshold rule for SDC of population count tables. M. Camden et al. calculate measures for utility and safety assessing the quality of this SDC concept. J.J. Salazar (Univerity La Laguna) explains advantages and disadvantages of the mathematical models for Controlled (Integer) Rounding vs. (continous) Tabular Adjustment. © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Frequency Tables C. Dwork et al. (Microsoft Research) suggest a new concept for disclosure risk avoidance for frequency data - “differential privacy” – and techniques to ensure it. These techniques are based on adding noise to Fourier coefficients corresponding to a given contingency table. A new method for assessing disclosure risk (i.e. risk of attribute disclosure) for tables of counts, the subtraction - attribution probability (SAP) method has been proposed by D. Smith and M. Elliot (University of Manchester). How N. to Shlomo of Southampton) embed(University the SAP method into an SDCcompares strategy? the performance of several techniques to protect population counts tables with respect to disclosure risk and information loss. Statistics New Zealand uses Random Rounding, a mean cell size rule and a threshold rule for SDC of population count tables. M. Camden et al. calculate measures for utility and safety assessing the quality of this SDC concept. J.J. Salazar (Univerity La Laguna) explains advantages and disadvantages of the mathematical models for Controlled (Integer) Rounding vs. (continous) Tabular Adjustment. © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Frequency Tables C. Dwork et al. (Microsoft Research) suggest a new concept for disclosure risk avoidance for frequency data - “differential privacy” – and techniques to ensure it. These techniques are based on adding noise to Fourier coefficients corresponding to a given contingency table. A new method for assessing disclosure risk (i.e. risk of attribute disclosure) for tables of counts, the subtraction - attribution probability (SAP) method has been proposed by D. Smith and M. Elliot (University of Manchester). N. Shlomo (University of Southampton) compares the performance of several techniques to protect population counts tables with respect to disclosure risk and information loss. Cell Statistics New Zealand uses Random Rounding, a mean cell size rule suppression/simple Imputation least distortion – Cell suppression best and a threshold rule for SDC of population count tables. M. Camden et al. method??? calculate measures for utility and safety assessing the quality of this SDC concept. J.J. Salazar (Univerity La Laguna) explains advantages and disadvantages of the mathematical models for Controlled (Integer) Rounding vs. (continous) Tabular Adjustment. © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Frequency Tables C. Dwork et al. (Microsoft Research) suggest a new concept for disclosure risk avoidance for frequency data - “differential privacy” – and techniques to ensure it. These techniques are based on adding noise to Fourier coefficients corresponding to a given contingency table. A new method for assessing disclosure risk (i.e. risk of attribute disclosure) for tables of counts, the subtraction - attribution probability (SAP) method has been proposed by D. Smith and M. Elliot (University of Manchester). N. Shlomo (University of Southampton) compares the performance of several techniques to protect population counts tables with respect to disclosure risk and information loss. Statistics New Zealand uses Random Rounding, a mean cell size rule and a threshold rule for SDC of population count tables. M. Camden et al. calculate measures for utility and safety assessing the quality of this SDC concept. J.J. Salazar (Univerity La Laguna) explains advantages and disadvantages of the mathematical models for Controlled (Integer) Rounding vs. (continous) Tabular Adjustment. Do integrality problems matter for magnitude tables? Could variable controlled rounding be modelled (efficiently)? © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Magnitude Tables The US Census Bureau adds noise to the underlying microdata prior to tabulation. The paper by L. Zayatz also addresses other SDC research areas at the USBC like synthetic micro data generation (also used to protect frequency tabular data) and a remote microdata analysis system. L. reactions? Cox (US NCHS) compares properties of two methods for Controlled User Tabular Adjustment, one based on LP technology, the other on iterative proportional fitting. Using tabular structures of EIA publications, and artificial microdata, R. Dandekar compares empirically the performance of various methods for tabular data protection, i.e. CTA, USBC’s noise method and cell suppression. P.P. de Wolf (CBS Netherlands) discusses a possible way to describe a simple class of linked tables that is often considered at NSI's. Web Access The USDA Economic Research Service has developed web-based data delivery tools for access to farm survey data (M. Morchart, C. Towe) © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Magnitude Tables The US Census Bureau adds noise to the underlying microdata prior to tabulation. The paper by L. Zayatz also addresses other SDC research areas at the USBC like synthetic micro data generation (also used to protect frequency tabular data) and a remote microdata analysis system. L. Cox (US NCHS) compares properties of two methods for Controlled Tabular Adjustment, one based on LP technology, the other on iterative proportional fitting. Using tabular structures of EIA publications, and artificial microdata, R. Dandekar compares empirically the performance of various methods for tabular data protection, i.e. CTA, USBC’s noise method and cell suppression. P.P. de Wolf (CBS Netherlands) discusses a possible way to describe a simple class of linked tables that is often considered at NSI's. Any plans for Linked Tables version of t-ARGUS HiTaS? Web Access The USDA Economic Research Service has developed web-based data delivery tools for access to farm survey data (M. Morchart, C. Towe) © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Summary of Topic ii (Tabular Data Protection) Magnitude Tables The US Census Bureau adds noise to the underlying microdata prior to tabulation. The paper by L. Zayatz also addresses other SDC research areas at the USBC like synthetic micro data generation (also used to protect frequency tabular data) and a remote microdata analysis system. L. Cox (US NCHS) compares properties of two methods for Controlled Tabular Adjustment, one based on LP technology, the other on iterative proportional fitting. Using tabular structures of EIA publications, and artificial microdata, R. Dandekar compares empirically the performance of various methods for tabular data protection, i.e. CTA, USBC’s noise method and cell suppression. P.P. de Wolf (CBS Netherlands) discusses a possible way to describe a simple class of linked tables that is often considered at NSI's. Web Access The USDA Economic Research Service has developed web-based data delivery tools for access to farm survey data (M. Morchart, C. Towe) Details on cell suppression approach within the tool? © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden Discussion/Questions to the authors Dwork et al.: Limitations (No of variables/Categories)? Could other SDC methods ensure differential privacy? Applicable to tabulations from a survey with hundreds of variables? Smith/Elliot: How to embed the SAP method into an SDC strategy? Shlomo: Cell suppression+simple imputation least distortion – Cell suppression best method??? Salazar: Do integrality problems matter for magnitude tables? Is variable controlled rounding a realistic option? Zayatz: User reactions? De Wolf: Any plans for Linked Tables version of t-ARGUS HiTaS? Morehart/Towe: Details on cell suppression approach within the tool? © Statistisches Bundesamt, IIA - Mathematisch Statistische Methoden