Aaron Johnson

Matthew Traudt, Rob Jansen, Aaron Johnson

The Tor network uses a measurement system to estimate its relays' forwarding capacity and to balance traffic among them. This system has been shown to be vulnerable to adversarial manipulation. Moreover, its accuracy and effectiveness in benign circumstances has never been fully quantified. We first obtain such a quantification by analyzing Tor metrics data and performing experiments on the live network. Our results show that Tor currently underestimates its true capacity by about 50% and improperly balances its traffic by 15-25%. Then, to solve the problems with security and accuracy, we present FlashFlow, a system to measure the capacity of Tor relays. Our analysis shows that FlashFlow limits a malicious relay to obtaining a capacity estimate at most 1.33 times its true capacity. Through realistic Internet experiments, we find that FlashFlow measures relay capacity with at least 89% accuracy 95% of the time. Through simulation, we find that FlashFlow can measure the entire Tor network in less than 5 hours using 3 measurers with 1 Gbit/s of bandwidth each. Finally, simulations using FlashFlow for load balancing shows that, compared to TorFlow, network weight error decreases by 86%, while the median of 50 KiB, 1 MiB, and 5 MiB transfer times decreases by 15%, 29%, and 37%, respectively. Moreover, FlashFlow yields more consistent client performance: the median rate of transfer timeouts decreases by 100%, while the standard deviation of 50 KiB, 1 MiB, and 5 MiB transfer times decreases by 55%, 61%, and 41%, respectively. We also find that the performance improvements increase relative to TorFlow as the total client-traffic load increases, demonstrating that FlashFlow is better suited to supporting network growth.

Akshaya Mani, T Wilson-Brown, Rob Jansen et al.

The Tor anonymity network is difficult to measure because, if not done carefully, measurements could risk the privacy (and potentially the safety) of the network's users. Recent work has proposed the use of differential privacy and secure aggregation techniques to safely measure Tor, and preliminary proof-of-concept prototype tools have been developed in order to demonstrate the utility of these techniques. In this work, we significantly enhance two such tools--PrivCount and Private Set-Union Cardinality--in order to support the safe exploration of new types of Tor usage behavior that have never before been measured. Using the enhanced tools, we conduct a detailed measurement study of Tor covering three major aspects of Tor usage: how many users connect to Tor and from where do they connect, with which destinations do users most frequently communicate, and how many onion services exist and how are they used. Our findings include that Tor has ~8 million daily users (a factor of four more than previously believed) while Tor user IPs turn over almost twice in a 4 day period. We also find that ~40% of the sites accessed over Tor have a torproject.org domain name, ~10% of the sites have an amazon.com domain name, and ~80% of the sites have a domain name that is included in the Alexa top 1 million sites list. Finally, we find that ~90% of lookups for onion addresses are invalid, and more than 90% of attempted connections to onion services fail.

Ryan Wails, Yixin Sun, Aaron Johnson et al.

Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; path-selection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client's ISP to a malicious host or ISP. The adversary behind each attack is relatively weak - generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.

Aaron D. Jaggard, Aaron Johnson, Paul Syverson et al.

Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. The major components of this system are (i) an ontology of network-element types that represents the main threats to and vulnerabilities of anonymous communication over Tor, (ii) a formal language that allows users to naturally express trust beliefs about network elements, and (iii) a conversion procedure that takes the ontology, public information about the network, and user beliefs written in the trust language and produce a Bayesian Belief Network that represents the probability distribution in a way that is concise and easily sampleable. We also present preliminary experimental results that show the distribution produced by our system can improve security when employed by users; further improvement is seen when the system is employed by both users and services.