Personalized Privacy-Preserving Routing Mechanism Design in Payment Channel Networks

Payment Channel Network (PCN) provides the off-chain settlement of transactions. It is one of the most promising solutions to solve the scalability issue of blockchain. Many routing techniques in PCN have been proposed. However, both incentive attack and privacy protection have not been considered in existing studies. In this paper, we present an auction-based system model for PCN routing using Laplace differential privacy mechanism. We formulate the cost optimization problem to minimize the path cost under the constraints of Hashed Time-Lock Contract (HTLC) tolerance and channel capacity. We propose an approximation algorithm to find the top K shortest paths constrained by the HTLC tolerance and channel capacity, i.e., top K-restricted shortest paths, and design the probability comparison function to find the path with the largest probability of having the lowest path cost among top K-restricted shortest paths as the final path. Moreover, we apply the binary search to calculate the transaction fee of users. Through both theoretical analysis and extensive simulations, we demonstrate that the proposed routing mechanism can guarantee the truthfulness and individual rationality with probabilities of 1/2 and 1/4, respectively. It can also ensure the differential privacy of users. The experiments on the real-world datasets demonstrate that the designed mechanism shows outstanding privacy protection level with only 13.2% more path cost comparing with the algorithm without privacy protection on average.