FDI: Quantifying Feature-based Data Inferability
This research addresses privacy and security concerns in applications like network forensics and data de-anonymization, but appears incremental as it builds on existing feature-based inference systems.
The paper tackles the problem of quantifying Feature-based Data Inferability (FDI) to determine conditions under which a desired fraction of users can be inferred, and evaluates this in network traffic attribution and data de-anonymization cases.
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.