Federated learning (FL) is a promising paradigm for collaboratively training networks on distributed clients while retaining data locally. Recent work has shown that personal data can be recovered even though clients only send gradients to the server. To against the gradient leakage issue, differential privacy (DP)-based solutions are proposed to protect data privacy by adding noise to the gradient before sending it to the server. However, the introduced noise affects the training efficiency of local clients, resulting in low model accuracy. Moreover, the identity privacy of clients has not been seriously considered in FL. In this article, we propose FL2DP, a privacy-preserving scheme focusing on protecting the data privacy as well as the identity privacy of clients. Different from the current schemes that add noise sampled from the Gaussian or Laplace distribution, in our scheme the noise is added to the gradient based on the exponential mechanism to achieve high training efficiency. Then, clients upload the perturbed gradients to a shuffler, which reassigns these gradients with different identities. We give a formal privacy definition called gradient indistinguishability to provide strict unlinkability for gradients shuffle. We propose a new gradient shuffling mechanism by adapting the DP-based exponential mechanism to satisfy gradient indistinguishability using the designed utility function. In this case, an attacker cannot infer the real identity of the client via the shuffled gradient. We conduct extensive experiments on two real-world datasets, and the results demonstrate the effectiveness of the proposed scheme.

Nowadays, a large number of Knowledge Graph Completion (KGC) methods have been proposed by using embedding based manners, to overcome the incompleteness problem faced with knowledge graph (KG). One important recent innovation in Natural Language Processing (NLP) domain is the employ of deep neural models that make the most of pre-training, culminating in BERT, the most popular example of this line of approaches today. Recently, a series of new KGC methods introducing a pre-trained language model, such as KG-BERT, have been developed and released compelling performance. However, previous pre-training based KGC methods usually train the model by using simple training task and only utilize one-hop relational signals in KG, which leads that they cannot model high-order semantic contexts and multi-hop complex relatedness. To overcome this problem, this article presents a novel pre-training framework for KGC task, which especially consists of both one-hop relation level task (low-order) and multi-hop meta-graph level task (high-order). Hence, the proposed method can capture not only the elaborate sub-graph structure but also the subtle semantic information on the given KG. The empirical results show the efficiency of the proposed method on the widely used real-world datasets.

This paper presents a method for reconstructing globally consistent 3D High-Definition (HD) maps at city scale. Current approaches for eliminating cumulative drift are mainly based on the pose graph optimization under the constraint of scan-matching factors. The misaligned edges in the graph may have negative impacts on the results. To address this problem and further handle inconsistency caused by multi-task acquisitions in urban environments, we introduce a refined structure of the factor graph considering systematical initialization bias, where the scan-matching factors are twice validated through a novel classifier and a robust optimization strategy. In addition, we incorporate a multi-hypothesis extended Kalman filter (MH-EKF) to remove dynamic objects. Quantitative experimental results demonstrate that the proposed method outperforms state-of-the-art techniques in terms of map quality.

Ride-hailing service solves the issue of taking a taxi difficultly in rush hours. It is changing the way people travel and has had a rapid development in recent years. Since the service is offered over the Internet, there is a great deal of uncertainty about security and privacy. Focusing on the issue, we changed payment pattern of existing systems and designed a privacy protection ride-hailing scheme. E-cash was generated by a new partially blind signature protocol that achieves e-cash unforgeability and passenger privacy. Particularly, in the face of a service platform and a payment platform, a passenger is still anonymous. Additionally, a lightweight hash chain was constructed to keep e-cash divisible and reusable, which increases practicability of transaction systems. The analysis shows that the scheme has small communication and computation costs, and it can be effectively applied in the ride-hailing service with privacy protection.