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- W2075291208 abstract "Over the last decade great strides have been made in developing techniques to compute functions privately. In particular, Differential Privacy gives strong promises about conclusions that can be drawn about an individual. In contrast, various syntactic methods for providing privacy (criteria such as k-anonymity and l-diversity) have been criticized for still allowing information of an individual to be inferred. In this paper, we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier. We demonstrate that even under Differential Privacy, such classifiers can be used to infer private attributes accurately in realistic data. We compare this to similar approaches for inference-based attacks on other forms of anonymized data. We show how the efficacy of all these attacks can be measured on the same scale, based on the probability of successfully inferring a attribute. We observe that the accuracy of inference of attributes for differentially data and $l$-diverse data can be quite similar." @default.
- W2075291208 created "2016-06-24" @default.
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- W2075291208 date "2011-01-01" @default.
- W2075291208 modified "2023-09-27" @default.
- W2075291208 title "Personal privacy vs population privacy" @default.
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- W2075291208 doi "https://doi.org/10.1145/2020408.2020598" @default.
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