A flexible in-network IP anonymization service

User privacy on the Internet has been an increasing concern in recent years. With the proliferation and sophistication of information services, data mining, and search engines, a simple network address may be used to reveal a great deal of information about a user, including location, identity, and behavior. Existing approaches to privacy, however, make unacceptable tradeoffs between performance and anonymity. For example, Tor 5 attempts to provide strong anonymity by withholding trust from third-party relays. We believe an acceptable level of privacy can be provided to most users, with noticeably lower latency and throughput impact, by working with the network provider. In this paper, we introduce AnonyFlow, an in-network anonymization service designed to efficiently and seamlessly provide privacy to users as they communicate with other endpoints and services. We design, implement, and evaluate an OpenFlow-based prototype of AnonyFlow that achieves endpoint anonymity without compromising on throughput or latency.