Online ride-hailing (ORH) services have become indispensable for our travel needs, offering the convenience of easily locating the nearest driver for riders through ride matching algorithms. However, existing ORH systems, such as Lyft and Didi, require users (both riders and drivers) to disclose their real-time location information during the matching process, thus giving rise to serious privacy concerns. Despite the proposal of various privacy-preserving ride-matching schemes, they remain insufficient in addressing potential malicious behaviors from the ORH server, such as colluding with designated drivers and deviation from computation protocols to interfere with the matching process. These behaviors lead to non-optimal matching results for riders. To address these issues, we present EMPRide, an efficient and privacy-preserving ride-matching scheme resistant to malicious ORH server. In EMPRide, we design an efficient and accurate computation of distances between users protocol, which integrates road network embedding and secure two-party computation. Additionally, we design a verification protocol that allows riders to verify the correctness of computed distances and matching results. Crucially, the communication overhead for riders in EMPRide remains constant, irrelevant to the number of available drivers. Our evaluation using real-world datasets demonstrates that EMPRide significantly outperforms existing solutions. Specifically, under identical conditions, in EMPRide, the computation speed on the ORH server is$19.22\times $faster and the communication cost is$8.08\times $less than state-of-the-art approaches. Moreover, riders experience a speed improvement of 4.84 orders of magnitude with$1.30\times $less communication, while drivers benefit from a 4.79 orders of magnitude speed increase with$1.45\times $less communication.

Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private gradients and models are not leaked to third parties. Many solutions have been proposed to address these issues, while solving the above three problems simultaneously is seldom considered. In this paper, we propose a publicly auditable and privacy-preserving federated learning scheme that is resistant to malicious participants uploading gradients with wrong directions and enables anyone to audit and verify the correctness of the training process. In particular, we design a robust aggregation algorithm capable of detecting gradients with wrong directions from malicious participants. Then, we design a random vector generation algorithm and combine it with zero sharing and blockchain technologies to make the joint training process publicly auditable, meaning anyone can verify the correctness of the training. Finally, we conduct a series of experiments, and the experimental results show that the model generated by the protocol is comparable in accuracy to the original FL approach while keeping security advantages.