This article formalises watchtower and its different properties includingagility,privacyagainst both watchtower and third parties,fairnesswith respect to both watchtower and its client andcoverage. We also evaluate the existing schemes regarding these properties and show they cannot achieve all properties altogether. Furthermore, we prove that there is a trade-off between the level of fairness that a watchtower provides to its clients and the coverage it can achieve. We also introduce FPPW, the first Fair and Privacy-Preserving Watchtower for Bitcoin. This new scheme provides fairness with respect to all channel participants including both channel parties and the watchtower. It means the funds of any honest channel participants are safe even assuming that the other two participants are corrupted and/or collude with each other. Furthermore, the watchtower in FPPW learns no information about the off-chain transactions and hence FPPW provides privacy against the watchtower. We also show that FPPW coverage, i.e., the total capacity of channels that an FPPW watchtower can cover, is higher than that of PISA and Cerberus and FPPW can be implemented without any update in the Bitcoin script.

Payment Channel Hub (PCH) is a promising solution to the scalability issue of first-generation blockchains or cryptocurrencies such as Bitcoin. It supports off-chain payments between a sender and a receiver through an intermediary (called the tumbler). Relationship anonymity and value privacy are desirable features of privacy-preserving PCHs, which prevent the tumbler from identifying the sender and receiver pairs as well as the payment amounts. To our knowledge, all existing Bitcoin-compatible PCH constructions that guarantee relationship anonymity allow only a (predefined) fixed payment amount. Thus, to achieve payments with different amounts, they would require either multiple PCH systems or running one PCH system multiple times. Neither of these solutions would be deemed practical.In this paper, we propose the first Bitcoin-compatible PCH that achieves relationship anonymity and supports variable amounts for payment. To achieve this, we have several layers of technical constructions, each of which could be of independent interest to the community. First, we propose BlindChannel, a novel bi-directional payment channel protocol for privacy-preserving payments, where one of the channel parties is unable to see the channel balances. Then, we further propose BlindHub, a three-party (sender, tumbler, receiver) protocol for private conditional payments, where the tumbler pays to the receiver only if the sender pays to the tumbler. The appealing additional feature of BlindHub is that the tumbler cannot link the sender and the receiver while supporting a variable payment amount. To construct BlindHub, we also introduce two new cryptographic primitives as building blocks, namely Blind Adaptor Signature (BAS), and Flexible Blind Conditional Signature (FBCS). BAS is an adaptor signature protocol built on top of a blind signature scheme. FBCS is a new cryptographic notion enabling us to provide an atomic and privacy-preserving PCH. Lastly, we instantiate both BlindChannel and BlindHub protocols and present implementation results to show their practicality.

In this paper we present an automatic vehicle ingress/egress counting method by clustering dense trajectories extracted from monitoring videos. Dense trajectories are extracted based on dense optical flow when passengers cross the door of the vehicle, and then clustered into different groups according to their descriptors with each legitimate group as a passenger. The contribution of the proposed method is twofold. First, we put forward an online passenger counting framework which is based on feature-points tracking and can be easily deployed to different scenarios. The method works even in low illumination conditions as demonstrated in experiments. Second, vehicle running information was combined to improve the accuracy of passenger counting. The transit vehicle settings are unconstrained and complex due to variations from illumination, movement and uncontrolled passenger behaviors. We tackle this by incorporating different modalities besides videos such as the status of the vehicle (e.g., in motion or not). The experimental results on multiple real bus videos show that the proposed system can count passengers with average accuracy of 94.9% at an average frame rate of 38 fps.