Helping Users Understand Their Web Footprints

Lisa Singh, Grace Hui Yang, Micah Sherr, Yifang Wei, Andrew Hian-Cheong, Kevin Tian, Janet Zhu, Sicong Zhang, Tavish Vaidya, Elchin Asgarli

Abstract: 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.