  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.
