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Summary of Topic ii (Tabular Data Protection)

Summary of Topic ii (Tabular Data Protection). Frequency Tables Magnitude Tables Web Access. Summary of Topic ii (Tabular Data Protection). Frequency Tables

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Summary of Topic ii (Tabular Data Protection)

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  1. Summary of Topic ii (Tabular Data Protection) • Frequency Tables • Magnitude Tables • Web Access

  2. 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. Limitations (No of variables/Categories)? Could other SDC methods ensure differential privacy? Applicable to tabulations from a survey with hundreds of variables?

  3. 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. How to embed the SAP method into an SDC strategy?

  4. 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. Cell suppression/simple Imputation least distortion – Cell suppression best method???

  5. 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)?

  6. 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) User reactions?

  7. 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) Any plans for Linked Tables version of t-ARGUS HiTaS?

  8. 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?

  9. 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?

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