Helping Users Understand Their Web Footprints

To help users better understand the potential risks associated with publishing data publicly, and the types of data that can be inferred by combining data from multiple online sources, we introduce a novel information exposure detection framework that generates and analyzes the web footprints users leave across the social web. We propose to use probabilistic operators, free text attribute extraction, and a population-based inference engine to generate the web footprints. Evaluation over public profiles from multiple sites shows that our framework successfully detects and quantifies information exposure using a small amount of non-sensitive initial knowledge.