Abstract
To build anonymization, the data anonymizer must determine the following three issues: Firstly, which data to be preserved? Secondly, which adversary background knowledge used to disclosure the anonymized data? Thirdly, The usage of the anonymized data? We have different anonymization techniques from the previous three-question according to different adversary background knowledge and information usage (information utility). In other words, different anonymization techniques lead to different information loss. In this paper, we propose a general framework for the utility-based anonymization to minimize the information loss in data published with a trade-off grantee of achieving the required privacy level.
Highlights
Publishing data in its original raw material may lead to privacy-breached issues
This paper aims to propose a general framework for utility-based anonymization to keep the data's privacy from one side and minimize the information loss of the data utility from the other side
We have proposed a brief literature survey of the different data utilities that lead to different data anonymization techniques
Summary
The data owner's different sources of data may contain a piece of sensitive information that needed to be preserved and protected from any attacking performed by anyone have any piece of background knowledge. To publish such valuable data for market-based analysis purposes, such data may be needed to be anonymized to keep the data privacy from and breaches issues may occur. This paper aims to propose a general framework for utility-based anonymization to keep the data's privacy from one side and minimize the information loss of the data utility from the other side.
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