Payment channel networks (PCNs) have been proposed to tackle the scalability issues in blockchains by enabling off-chain transaction settlement. However, the balance depletion problem caused by unidirectional transactions may jeopardize the payments in PCNs. Existing works address this problem by sending artificial payments to rebalance the payment channels. In this paper, we take advantage of a unique property in PCNs, where the payments of opposite directions between two users can cancel each other out, to mitigate this channel depletion problem. Specifically, we design BAR, a distributed balance-aware payment routing protocol, subject to fee-based conservation, timeliness, and feasibility constraints. Moreover, to ensure payment security, we modify the original Hashed Time-Lock Contract (HTLC) protocol to adapt it to BAR, such that BAR achieves efficiency and atomicity. Extensive simulations demonstrate that BAR outperforms the state-of-the-art algorithms Spider [1] and LND [2] in terms of success ratio and success volume.

Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, a significant barrier is that a PCN user must solicit sufficient capital owned by the counterparty on its channel (i.e., inbound liquidity) to receive payments. To alleviate this inbound liquidity problem, Channel Liquidity Marketplaces (CLMs), e.g., Bitcoin's Lightning Pool, have been introduced, such that users can buy and sell inbound liquidity by trading crypto capital commitment in PCNs. Existing CLMs lack good incentive mechanisms that can attract more user participation. To fulfill this void, we design Cumulonimbus, an incentive mechanism for trading crypto capital commitment, which satisfies truthfulness, individual rationality, budget balance, and computational efficiency. Particularly, Cumulonimbus considers two unique features of crypto capital commitment, referred to as demand indivisibility and supply divisibility. Extensive simulations demonstrate that Cumulonimbus achieves higher satisfaction ratio, liquidity utilization, and social welfare compared with a state-of-the-art CLM mechanism Lightning Pool [18].

Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, PCN introduces an undesirable assumption that a channel participant must stay online and be synchronized with the blockchain to defend against frauds. To alleviate this issue, watchtowers have been introduced, such that a hiring party can employ a watchtower to monitor the channel for fraud. However, a watchtower might profit from colluding with a cheating counterparty and fail to perform this job. Existing solutions either focus on heavy cryptographic techniques or require a large collateral. In this work, we leverage smart contracts through economic approaches to counter collusions for watchtowers in PCNs. This brings distrust between the watchtower and the counterparty, so that rational parties do not collude or cheat. We provide detailed analyses on the contracts and rigorously prove that the contracts are effective to counter collusions with minimal on-chain operations. In particular, a watchtower only needs to lock a small collateral, which incentivizes participation of watchtowers and users. We also provide an implementation of the contracts in Solidity and execute them on Ethereum to demonstrate the scalability and efficiency of the contracts.

The past decade has witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. However, transaction failures may occur due to external attacks or unexpected conditions, e.g., an uncooperative user becoming unresponsive. In this paper, we present a distributed robust payment routing protocol RobustPay+to resist transaction failures, which achieves robustness, efficiency, distributedness and approximate optimization. Specifically, we investigate the problem of robust routing in PCNs from an optimization perspective, which is to find a pair of payment paths for a payment request, while minimizing the worst-case transaction fee, subject to the timeliness and feasibility constraints. We present a distributed 2-approximation algorithm for this problem. Moreover, we modify the original Hashed Time-lock Contract (HTLC) protocol and adapt it to the robust payment routing protocol to achieve robustness and efficiency. Extensive simulations demonstrate that RobustPay+significantly outperforms baseline algorithms in terms of the success ratio and the average accepted value.

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques, e.g., k-anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attention, provable good algorithms considering the tradeoff between location privacy and location information quality from the optimization perspective in crowdsensing are lacking in the literature. In this article, we study two related optimization problems from two different perspectives. The first problem is to minimize the location quality degradation caused by the protection of users' location privacy. We present an efficient optimal algorithm OLoQ for this problem. The second problem is to maximize the number of protected users, subject to a location quality degradation constraint. To satisfy the different requirements of the platform, we consider two cases for this problem: 1) overlapping and 2) nonoverlapping perturbations. For the former case, we give an efficient optimal algorithm OPUMO. For the latter case, we first prove its NP-hardness. We then design a (1-E)-approximation algorithm NPUMNand a fast and effective heuristic algorithm HPUMN. Extensive simulations demonstrate that OLoQ, OPUMO, and HPUMNsignificantly outperform an existing algorithm.

The past decade has seen vast research on the influence maximization problem in social networks: How to select a subset of individuals to become initial adopters, so that the word-of-mouth effect in the social network is maximized Approximation algorithms have been proposed for this NP-hard problem with knapsack or other constraints. To incentivize influencers to become initial adopters, Singer has initiated budget feasible mechanisms. In this paper, we generalize them to the budget feasible mechanism for k-topic influence maximization problem. We investigate this problem and propose KIMI. We rigorously prove that KIMI achieves 5e/(e-1) approximation and computational efficiency, individual rationality, truthfulness, budget feasibility. Extensive simulations demonstrate that KIMI significantly outperforms baseline methods.

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.

The past several years have witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. In this paper, we investigate the problem of payment routing in PCNs from an optimization perspective, which is to minimize the transaction fee of a payment path, subject to the timeliness and feasibility constraints. We present an optimal distributed algorithm CheaPay for this problem. Extensive simulations demonstrate that CheaPay significantly outperforms baseline algorithms in terms of the success ratio and the average accepted value.

The past decade has witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. However, transaction failures may occur due to external attacks or unexpected conditions, e.g., an uncooperative user becoming unresponsive. In this paper, we present a distributed robust payment routing protocol RobustPay to resist transaction failures, which achieves robustness, efficiency and distributedness. Moreover, we modify the original HTLC protocol and adapt it to the robust payment routing protocol.

Spectrum auctions provide a platform for licensed spectrum users to share their underutilized spectrum with unlicensed users. Existing spectrum auctions either use the protocol interference model to characterize interference relationship as binary relationship, or do not allow the primary and secondary users to share channels simultaneously. To fill this void, we design SPA, a spectrum single-sided auction under the physical interference model, which considers the interference to be accumulative. We prove that SPA is truthful, individually rational, and computationally efficient. Results from extensive simulation studies demonstrate that, SPA achieves higher spectrum utilization and buyer satisfaction ratio, compared with an existing auction adapted for the physical interference model.