Vi bøger
Levering: 1 - 2 hverdage
Forlænget returret til d. 31. januar 2025

Privacy-Preserving Data Publishing - Ada Wai-Chee Fu - Bog

Bag om Privacy-Preserving Data Publishing

Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions

Vis mere
  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031007064
  • Indbinding:
  • Paperback
  • Sideantal:
  • 140
  • Udgivet:
  • 22. februar 2010
  • Størrelse:
  • 191x8x235 mm.
  • Vægt:
  • 277 g.
  • 8-11 hverdage.
  • 15. januar 2025
På lager
Forlænget returret til d. 31. januar 2025
  •  

    Kan ikke leveres inden jul.
    Køb nu og print et gavebevis

Normalpris

Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Privacy-Preserving Data Publishing

Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions

Brugerbedømmelser af Privacy-Preserving Data Publishing



Gør som tusindvis af andre bogelskere

Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.