Towards Predicting Efficient and Anonymous Tor Circuits

The Tor anonymity system provides online privacy for millions of users, but it is slower than typical web browsing. To improve Tor performance, we propose PredicTor, a path selection technique that uses a Random Forest classifier trained on recent measurements of Tor to predict the performance of a proposed path. If the path is predicted to be fast, the client then builds a circuit using those relays. We implemented PredicTor in the Tor source code and show through live Tor experiments and Shadow simulations that PredicTor improves Tor network performance by 11% to 23% compared to Vanilla Tor and by 7% to 13% compared to the previous state-of-the-art scheme. Our experiments show that PredicTor is the first path selection algorithm to dynamically avoid highly congested nodes during times of high congestion and avoid long-distance paths during times of low congestion. We evaluate the anonymity of PredicTor using standard entropy-based and time-to-first-compromise metrics, but these cannot capture the possibility of leakage due to the use of location in path selection. To better address this, we propose a new anonymity metric called CLASI: Client Autonomous System Inference. CLASI is the first anonymity metric in Tor that measures an adversary’s ability to infer client Autonomous Systems (ASes) by fingerprinting circuits at the network, country, and relay level. We find that CLASI shows anonymity loss for location-aware path selection algorithms, where entropy-based metrics show little to no loss of anonymity. Additionally, CLASI indicates that PredicTor has similar sender AS leakage compared to the current Tor path selection algorithm due to PredicTor building circuits that are independent of client location

TxProbe: Discovering Bitcoin’s Network Topology Using Orphan Transactions

Bitcoin relies on a peer-to-peer overlay network to broadcast transactions and blocks. From the viewpoint of network measurement, we would like to observe this topology so we can characterize its performance, fairness and robustness. However, this is difficult because Bitcoin is deliberately designed to hide its topology from onlookers. Knowledge of the topology is not in itself a vulnerability, although it could conceivably help an attacker performing targeted eclipse attacks or to deanonymize transaction senders. In this paper we present TxProbe, a novel technique for reconstructing the Bitcoin network topology. TxProbe makes use of peculiarities in how Bitcoin processes out of order, or “orphaned” transactions. We conducted experiments on Bitcoin testnet that suggest our technique reconstructs topology with precision and recall surpassing 90%. We also used TxProbe to take a snapshot of the Bitcoin testnet in just a few hours. TxProbe may be useful for future measurement campaigns of Bitcoin or other cryptocurrency networks

On the Claims of Weak Block Synchronization in Bitcoin

Recent Bitcoin attacks [CCS\u2721, CCS\u2721, ICDCS\u2719] commonly exploit the phenomenon of so-called weak block synchronization in Bitcoin. The attacks use two independently-operated Bitcoin monitors — i.e., Bitnodes and a system of customized supernodes — to confirm that block propagation in Bitcoin is surprisingly slow. In particular, Bitnodes constantly reports that around 30% of nodes are 3 blocks (or more) behind the blockchain tip and the supernodes show that on average more than 60% of nodes do not receive the latest block even after waiting for 10 minutes. In this paper, we carefully re-evaluate these controversial claims with our own experiments in the live Bitcoin network and show that block propagation in Bitcoin is, in fact, fast enough (e.g., most peers we monitor receive new blocks in about 4 seconds) for its safety property. We identify several limitations and bugs of the two monitors, which have led to these inaccurate claims about the Bitcoin block synchronization. We finally ask several open-ended questions regarding the technical and ethical issues around monitoring blockchain networks