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.
@article{arxiv.1902.00714,
title = {FDI: Quantifying Feature-based Data Inferability},
author = {Shouling Ji and Haiqin Weng and Yiming Wu and Qinming He and Raheem Beyah and Ting Wang},
journal= {arXiv preprint arXiv:1902.00714},
year = {2019}
}