EAN: 9783031007064

Bilder-Quelle: discount24.de - Sport-Freizeit

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

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24.03.2025 um 09:20 Uhr

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