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arXiv · 1902.00714

FDI: Quantifying Feature-based Data Inferability

Shouling Ji·Haiqin Weng·Yiming Wu·Qinming He·Raheem Beyah·Ting Wang
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Abstract

Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in this paper, we study the Feature-based Data Inferability (FDI) quantification problem. First, we conduct the FDI quantification under both naive and general data models from both a feature distance perspective and a feature distribution perspective. Our quantification explicitly shows the conditions to have a desired fraction of the target users to be Top-K inferable (K is an integer parameter). Then, based on our quantification, we evaluate the user inferability in two cases: network traffic attribution in network forensics and feature-based data de-anonymization. Finally, based on the quantification and evaluation, we discuss the implications of this research for existing feature-based inference systems.

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Shouling Ji, Haiqin Weng, Yiming Wu, Qinming He, Raheem Beyah, Ting Wang. 2019-06-03. FDI: Quantifying Feature-based Data Inferability. https://arxiv.org/abs/1902.00714

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