A Firm Foundation for Private Data Analysis.

The article presents a method for preserving the privacy of individuals while simultaneously making data sets containing their private information available for statistical analysis. The concept of differential privacy is said to offer a means of achieving statistical disclosure control, by randomiz...

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Publicado en:Communications of the ACM Vol. 54; no. 1; pp. 86 - 96
Autor principal: DWORK, CYNTHIA
Formato: Artículo
Publicado: Association for Computing Machinery Jan2011
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        Database security
        Data analysis
        Privacy
        Data protection
        Data security
        Computer security
        Algorithms
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          Database security
          Data analysis
          Privacy
          Data protection
          Data security
          Computer security
          Algorithms
      ab: The article presents a method for preserving the privacy of individuals while simultaneously making data sets containing their private information available for statistical analysis. The concept of differential privacy is said to offer a means of achieving statistical disclosure control, by randomizing responses in a way which preserves the accuracy of the data while dissociating it from the identity of any given individual. The hazards of auxiliary data, which can be used to indirectly ascertain redacted information, are noted, and the usefulness of pan-private algorithms is discussed.
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    language: English
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