Reinforcement Learning on Tor: Prioritizing Performance Compromises Anonymity

The onion routing (Tor) protects the IP and behavioral anonymity on the Internet by routing the networking packet through multiple randomly selected relay nodes between the source and the destination. It therefore adds transmission delays, and the latency cost is greater than not using Tor (i.e., when the packet is transmitted directly to the destination). Machine learning can be used to learn about the Tor circuit delays, given the source and destination, to identify the circuits with smaller latency cost. In this paper, however, we show how using reinforcement learning in its by-default, deterministic fashion can deprive anonymity. More specifically, choosing the performance-best circuit corresponding to the exploitation strategy provides no anonymity with an entropy of zero. Such approach can eliminate the anonymity protection and performs worse than disabling Tor altogether. We therefore recommend a strategic and appropriate application of machine learning which not only advances the performance but also take the anonymity into account for future research to advance Tor and Internet anonymity.