With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization and training efficiency. Specifically, its synchronous dependency design increases training latency, while resource and data heterogeneity among participants further hinder efficient computation. To overcome these challenges, we propose \texttt{PubSub-VFL}, a novel VFL paradigm with a Publisher/Subscriber architecture optimized for two-party collaborative learning with high computational efficiency. \texttt{PubSub-VFL} leverages the decoupling capabilities of the Pub/Sub architecture and the data parallelism of the parameter server architecture to design a hierarchical asynchronous mechanism, reducing training latency and improving system efficiency. Additionally, to mitigate the training imbalance caused by resource and data heterogeneity, we formalize an optimization problem based on participants’ system profiles, enabling the selection of optimal hyperparameters while preserving privacy. We conduct a theoretical analysis to demonstrate that \texttt{PubSub-VFL} achieves stable convergence and is compatible with security protocols such as differential privacy. Extensive case studies on five benchmark datasets further validate its effectiveness, showing that \texttt{PubSub-VFL} compared to state-of-the-art baselines not only accelerates training by $2 \sim 7\times$ without compromising accuracy but also achieves computational resource utilization by up to 91.07\%.

Recently, truth discovery in crowdsensing systems has received considerable attention with its appealing features for extracting truthful information from multiple unreliable data sources. However, it also poses new challenges to the issues of privacy and security. On the one hand, workers' sensed data can be used to infer their privacy. On the other hand, workers may be selfish and lazy, especially in the Internet-of-Things environment, devices are usually resource constrained, so they may dishonestly execute the costly sensing task so as to reduce resource consumption, or even break the protocol to obtain illegal rewards. Although some privacy-preserving truth discovery schemes have been proposed, they still cannot achieve strong privacy protection while keeping efficiency on the worker side, and still has no efficient incentive mechanism to persuade workers to participate in the system operations. In this article, we propose an incentive-based privacy-preserving truth discovery framework, named InPPTD. By adopting the Paillier homomorphic cryptosystem and two noncolluding servers, InPPTD not only effectively protects workers' sensed data information but also preserves the privacy of these workers' weight information. Meanwhile, a weight-based incentive mechanism is introduced in InPPTD to reduce the number of lazy workers. Security and performance analysis shows that InPPTD can guarantee stronger security features, while also ensure efficiency in terms of computation and communication overhead.

Secure data aggregation has been widely studied in the area of the smart grid. Many existing schemes have studied protecting user's privacy in data aggregation by using advanced cryptographic tools. However, they usually introduce a large computation burden to smart meters in limited computing power or require a trusted authority. How to ensure the efficiency on the user side while preserving user's privacy still has not been well addressed. In this article, we consider the scenario where there does not exist a trusted authority and users in the smart grid may dynamically change, and propose an efficient and robust data aggregation scheme without a trusted authority for the smart grid. Our proposed scheme not only ensures user's privacy and efficiency but also supports flexible dynamic user management with no need of involving a trusted authority. Analysis of security and performance shows that our scheme can guarantee stronger security features, while ensuring efficiency in terms of computation, communication, and storage overhead.

In power utility service outsourcing, some time-sensitive computations (e.g., dynamic prices prediction) are outsourced to a third-party service provider. This brings in new privacy threats to customers. Although some existing works focus on achieving privacy-preserving temporal and spatial aggregation for one center, they basically cannot be directly applied to the scenario of service outsourcing with multiple centers (e.g., with power utility and service providers). We thus propose a privacy-preserving service outsourcing scheme, called PPSO, for real-time pricing demand response in smart grid with fault tolerance and flexible customers' enrollment and revocation. In our proposed PPSO, power utility can outsource the dynamic pricing prediction to a service provider, while still preserving customers' privacy. Extensive experiment results demonstrate that PPSO has less computation overhead and lower transmission delay compared with existing schemes.

Nowadays, the Space Information Network (SIN) has been widely used in real life because of its advantages of communicating anywhere at any time. This feature is leading to a new trend that traditional wireless users are willing to roam to SIN to obtain a better service. However, the features of exposed links and higher signal latency in SIN make it difficult to design a secure and fast roaming authentication scheme for this new trend. Although some existing researches have been focused on designing secure authentication protocols for SIN or providing roaming authentication protocols for traditional wireless networks, these schemes cannot provide adequate requirements for the roaming communication in SIN and bring in critical issues, such as the privacy leakage or intolerable authentication delay. Observing these problems have not been well addressed, we design an anonymous and fast roaming authentication scheme for SIN. In our scheme, we utilize the group signature to provide the anonymity for roaming users, and assume that the satellites have limited computing capacity and make them have the defined authentication function to avoid the real-time involvement of the home network control center when authenticating the roaming users. The results of security and performance analysis show that the proposed scheme can provide the required security features, while providing a small authentication delay.

Privacy-preserving data aggregation has been extensively studied in smart grid. However, almost all existing schemes aggregate the total electricity consumption data of the whole user set, which sometimes cannot meet the fine-grained demands from control center in smart grid. In this paper, we propose a privacy-preserving multisubset data aggregation scheme, named PPMA, in smart grid. PPMA can aggregate users' electricity consumption data of different ranges, while guaranteeing the privacy of individual users. Detailed security analysis shows that PPMA can protect individual user's electricity consumption privacy against a strong adversary. In addition, extensive experiments results demonstrate that PPMA has less computation overhead and no more extra communication and storage costs.

Smart grid facilitates reliable and efficient power generation and transmission by integrating information and communication technologies. By collecting users' power demands in advance, the control center (power operator) can adjust the amount of electricity generated to reduce the excess power, which can increase the profit of the power operator. However, on the one hand, user's privacy becomes a critical issue, since it may leak out a user's life habits, which may make user's safety and belongings under threat. On the other hand, the system needs to arm the capability to avoid diverse adversaries' attacks and trace misbehaving users (who request power irresponsibly). In this paper, we propose a privacy-preserving and real-time traceable power request scheme to fulfill the security requirements. We utilize aggregator as a proxy between users and the control center, which verifies the messages and aggregates multiple users' requests together to preserve their privacy. More importantly, this privacy-preserving mechanism has no effect for the control center to whether charge each user, or trace the misbehaving users in real time. The performance analysis shows that our scheme is efficient in terms of computation and storage overhead.