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- W2912726248 abstract "Digital data on the Web are nowadays regarded significant sources of information for marketing and user profiling, etc. However, digital data are risky sources of privacy violation. To address privacy breaches, we can use differential privacy, which has become the de facto standard for privacy protection in statistical databases. However, problems need to be solved, including those related to noise parameter configuration, even before differential privacy can be applied into the real world. In this study, we introduce a linkage attack to identify a user with different nicknames for each subservice on a hue online portal service. In addition, we propose a configuration technique for the upper bound of noise parameter ε to prevent linkage attack. We demonstrate the linkage attack with experiments by using real-world online portal service data. Finally, we validate the proposed configuration technique." @default.
- W2912726248 created "2019-02-21" @default.
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- W2912726248 date "2018-12-01" @default.
- W2912726248 modified "2023-09-23" @default.
- W2912726248 title "DRAKO: Differentially pRivate Algorithm to meet K-anonymity for Online portal service" @default.
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- W2912726248 doi "https://doi.org/10.1109/bigdata.2018.8622476" @default.
- W2912726248 hasPublicationYear "2018" @default.
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