Graph neural networks (GNNs) are extensively employed in location-related scenarios, relying on aggregation to gather features from neighboring nodes based on edge weights. Features are closely bound to nodes’ locations and edge weights mirror distance correlations. In this sense, certain privacy concerns exist while providing location-based services if there is insufficient privacy protection. To this end, we propose a privacy-preserving and distance-aware data aggregation framework (PEDA) for GNNs. Specifically, PEDA achieves location privacy by combining circular-based positional coding with inner product functional encryption. Because of the masks in the codes, the decryption returns masked distances, preventing distance leakage. Following this, in order to protect feature privacy, we employ secret sharing. To preserve the collection strategy’s privacy, we implement an oblivious transfer for collecting the shared features. Additionally, we securely generate the adjacency matrix and aggregate features based on multi-party computation. Thorough security analysis and comprehensive evaluation demonstrate the privacy, feasibility and practicality of our approach. When compared to related works, PEDA offers four types of privacy, maintains distance awareness and feature utility, and allows for oblivious data collecting with little computational cost sacrifice.

With the commercial launch of 5G technology, the development of the Internet of Things, and the proliferation of edge computing, wireless networks are having a profound impact on society. However, ensuring data security in the wireless network remains a challenging issue. The proposed cloud-network-end collaborative security architecture provides an effective approach to address this challenge. This paper presents a non-interactive key agreement and update scheme based on cloud-network-end architecture. Non-interactive secure association is achieved by using Chameleon Hash and Diffie-Hellman key exchange technology. Furthermore, a Key Derivation Function is introduced to implement a one-time padding update mechanism. Security analysis in Protocol Composition Logic shows that the proposed scheme satisfies authentication, key confidentiality and forward security for session keys. Finally, experiments confirm that our solution incurs minimal communication overhead on the user side and achieves efficient secure association and key update.

As the core subject of IoT applications, IoT devices have faced numerous security challenges. Especially for IoT devices deployed in remote or harsh environments, they are often unattended for long periods, making it difficult to share the sensing data and susceptible to potential physical attacks. While aerial assistance methods represented by unmanned aerial vehicles (UAVs) can solve the problem of data sharing at a low cost, it is necessary to establish a secure channel between ground control stations, UAVs, and IoT devices due to the sensitivity of the sensing data. Recently, Physical Unclonable Function (PUF) has been proven to provide unique identity identification for devices using its tamper-proof feature. In this paper, we propose a lightweight UAV-assisted authentication and key agreement protocol for unattended IoT devices, ensuring secure communication and physical tamper-proof requirements. However, our work does not stop there. We noticed that some existing PUF-based authentication schemes misunderstand the ability of PUF, which leads to these schemes cannot actually provide physical protection. We analyzed the security vulnerabilities of these schemes and proposed rules that should be followed when designing authentication protocols using PUF. In addition, for the first time, we put forward the formal definitions and proof methods for PUF in the formal proof of the security protocol, which avoided the unreasonable initial assumptions adopted in the proof of the existing schemes. We extended Mao-Boyd (MB) logic and comprehensively analyzed the proposed protocol. We also evaluate the performance of the proposed scheme, and the results show that the proposed scheme has certain advantages in communication and computation overhead compared with existing schemes.

The widespread use of machine learning in location-related scenarios is propelling the rapid development of intelligent transportation. To assist users in making more informed travel plans, the demand for improving prediction accuracy is growing. Prior to model training, data cleaning is a common method used to eliminate redundant, erroneous and outlier samples. However, in intelligent transportation, there are serious issues with location awareness and privacy protection of existing data cleaning schemes. Therefore, we propose a location-aware and privacy-preserving data cleaning framework (PriSPA) which provides a cleaned dataset consisting of the samples from adopted data suppliers at qualified locations while ensuring the privacy of locations, spatial constraints and sensitive samples. We combine boolean secret sharing with XOR operations to make sure that it is possible to figure out whether a location complies with spatial constraints without leakage. More specifically, we ensure privacy using key agreement, secret sharing, authenticated encryption and random permutation. We seriously analyze the security of PriSPA and conduct comprehensive experiments to prove its security, effectiveness and efficiency. Based on the comparisons with the raw traffic forecasting framework, we observe that PriSPA improves the precision of the model with 17.6% - 32.7% error reduction.

With the rapid development of the Internet of Vehicles (IoV), location-aware outsourcing data aggregation is evolving into a fundamentally key role for analyzing a significant amount of data among smart devices. Due to the spatial property of the data in IoV, location privacy and data security in outsourcing data aggregation face critical challenges. Meanwhile, because of the diversity of entities in IoV, how aggregating data from multiple domains is also a serious issue. In this article, we propose a location-aware verifiable outsourcing data aggregation (LAVODA) for IoV where aggregators are hierarchical for the cross-chain mechanism in multiblockchains. With homomorphic encryption and homomorphic commitment, we achieve the verifiability of the aggregation while ensuring data confidentiality. Specifically, we combine twin-DH with circle-based location verification to ensure the privacy of the requester’s location strategy and data providers’ locations. The security analysis shows that our scheme can achieve the above secure properties. In addition, the experimental results demonstrate that our scheme is efficient and feasible in practice.

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.

Recently, some Unbiased Gradient Sampling-based (UGS) methods have been proposed to enhance the security and efficiency of federated learning through crafted unbiased random transformation and sampling, such as MinMax Sampling in SIGMOD ’22. In this paper, we propose a novel attack, GLAUS, to show that UGS is not as secure as claimed in these works and is still vulnerable to the gradient leakage attack (GLA). Specifically, we demonstrate an idea to approximately infer the gradient for GLA in the context of the UGS scenario where the real gradient is not available. Once the gradient is approximately obtained, the security of the UGS frameworks is downgraded to that of the original federated learning. The approximate gradient is refined by the following steps: 1)narrow the gradient searching rangeto the finite set; 2)obtain the magnitudeof each gradient value approximately; 3)revise the gradient signs. Versus the failure of existing attacks, extensive experiments on six datasets show that our attack is effective in reconstructing private datapoints with pixel-wise accuracy on four network sizes and three image resolutions. Finally, we show how to defend against GLAUS while maintaining the high efficiency of UGS and only introducing an additional step to hide the sampled gradient indices.

Due to the safe portability of the device in sixth generation (6G) enabled network in box (NIB) and the mobility of locations, users equipped with the device can have a better experience in spatial crowdsourcing. The integration of NIB with 6G results in a decentralized spatial crowdsourcing in industrial automation, but threatens the security of tasks and answers and also leads to the leakage of sensing nodes’ locations. To address these problems, in this article, we propose a secure decentralized spatial crowdsourcing scheme for 6G-enabled NIB (DSC-NIB). Using DSC-NIB, the control station and sensing nodes can gather and transmit information on the blockchain using NIB, without depending on the third party. The control station shares encrypted location strategy parameters set to negotiate session keys and a group key with sensing nodes whose locations satisfy the location strategy, while ensuring the privacy of sensing nodes’ locations. For the security of tasks and answers, we leverage the Counter with CBC-MAC authenticated encryption mechanism to provide confidentiality and integrity. Furthermore, we analyze the security of the proposed DSC-NIB. Compared with existing approaches, the performance is evaluated and improved by 30--50%. To further optimize the performance, two optimized schemes without sacrificing security are presented and our results demonstrate that performance is improved greatly.