# FDI: Quantifying Feature-based Data Inferability

## TL;DR

This paper introduces a method to quantify feature-based data inferability, analyzing conditions for successful user identification in security contexts and evaluating implications for privacy and security systems.

## Contribution

It provides a novel quantification framework for feature-based data inferability under different data models and applies it to real-world security scenarios.

## Key findings

- Explicit conditions for Top-K inferability
- Evaluation of user inferability in network forensics
- Implications for privacy-preserving inference systems

## 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.