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

FDI: Quantifying Feature-based Data Inferability