Year: 2020
Type: preprint
Abstract: The possibility of fingerprinting the search keywords issued by a user on popular web search engines is a significant threat to user privacy. This threat has received surprisingly little attention in the network traffic analysis literature. In this work, we consider the problem of keyword fingerprinting of HTTPS traffic -- we study the impact of several factors, including client platform diversity, choice of search engine, feature sets as well as classification frameworks. We conduct both closed-world and open-world evaluations using nearly 4 million search queries collected over a period of three months. Our analysis reveals several insights into the threat of keyword fingerprinting in modern HTTPS traffic.
Source: arXiv (Cornell University)
Authors: Yan, Junhua, Alan, Hasan Faik, Kaur, Jasleen
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3072560503