Yunguo Guan

Advancements in flexible electronics necessitate the development of corresponding testing apparatuses that can accommodate their unique deformation characteristics. Unlike traditional rigid electronics, flexible devices often experience complex, multi-directional deformations. This study focuses on the simultaneous application of bending and stretching deformations. We introduce an experimental apparatus that functions as an integrated component of a universal testing machine. It is designed to provide a uniform bending radius while allowing adjustable stretch induced strain for flexible electronic specimens. The apparatus is fabricated using 3D printing technology, enabling easy customization to accommodate various bending radii and dimensions specific to each specimen. Additionally, we analyzed the strain range for each design parameter to provide practical reference for future applications. To demonstrate the functionality of this experimental apparatus, we conduct tests on a flexible printed capacitor sensor, showcasing its effective performance under the specified deformations.

Industrial Internet of Things (IIoT) systems in sectors like manufacturing, energy, and healthcare are increasingly deployed in cloud-assisted operational environments, where network telemetry and security analytics are routinely processed in the cloud. However, these systems remain highly vulnerable to cyber threats from shared threat actors. Among these, low-rate Denial of Service (LDoS) attacks, marked by subtle periodic traffic patterns, are particularly challenging to detect when analyzed in isolation. Cross-organization collaborative detection across cloud platforms can improve identification accuracy, but sharing threat intelligence risks exposing sensitive operational information. To tackle this challenge, we propose Labelled-Threshold Private Set Intersection (LT-PSI), a cryptographic framework that allows two organizational clouds to securely identify common elements whose associated label vectors satisfy a similarity threshold, without revealing any additional data. Our LT-PSI protocol introduces an innovative combination of position encoding, Diffie-Hellman Oblivious Pseudorandom Functions (DH-OPRF), and Bloom filters, effectively transforming threshold-based label similarity matching into efficient and privacy-preserving set membership tests. Particularly, our protocol achieves sublinear online complexity and is well-suited for cloud execution, integrating an adaptive early termination strategy that significantly reduces the number of OPRF invocations. We provide formal security proofs under the semi-honest model and validate the protocol through extensive experiments across diverse similarity thresholds and dataset sizes. Results show that LT-PSI is significantly more efficient than brute-force threshold matching while preserving privacy. The framework naturally supports cloud-to-cloud collaborative security analytics and generalizes to broader cloud and edge threat intelligence scenarios requiring private, threshold-based feature matching.

The rapid development of blockchain technology has led to a constant increase in its financial and technological value. However, this has also led to malicious attacks. Distributed denial-of-service attacks pose a considerable threat to blockchain technology out of many attacks due to its effectiveness and distributed nature. To protect the blockchain from DDoS attacks, researchers have proposed a large number of defensive schemes. However, these schemes are not well-suited for use in practical situations. In this work, we propose a DDoS attack detection scheme based on centralized federated learning, where multiple participating nodes locally train models and upload them to a central node for aggregation. Additionally, we propose a more suitable method for blockchain scenarios, using decentralized federated learning technology, where multiple nodes exchange models in a peer-to-peer manner to complete model training without a central server. We simulate DDoS attacks in blockchain and generate a large dataset by combining it with traditional network layer DDoS attack data to evaluate the effectiveness of our schemes. The experimental results show that the proposed schemes perform well in classification accuracy, demonstrating that our techniques can detect DDoS attacks effectively.

The flourishing of intelligent connected vehicles (ICVs) has fostered the emergence of vehicular crowdsourcing (VCS) applications, in which ICVs function as workers to execute diverse spatio-temporal critical tasks. As a vital service of VCS, task scheduling aims to assign tasks to the most suitable workers. To cope with the escalation of service scale, the service provider tends to outsource the service to powerful cloud servers, which however triggers the privacy concerns of workers, task owners, and the service provider. Previously reported privacy-preserving task allocation schemes can mainly be divided into single-attribute-aware and multiattribute-aware schemes. Nevertheless, the former suffers from practicality issues, while the latter either fails to achieve single-dimensional privacy and access pattern privacy or introduces substantial computational costs. To tackle the above challenges, we propose an efficient privacy-preserving outsourced task scheduling scheme (EPTS) for VCS, in which two cloud servers can cooperate to efficiently and securely conduct multiattribute-aware task scheduling. To this end, we devise five lightweight secure two-party protocols under the additive secret sharing (ASS) setting, which enable cloud servers to obliviously filter workers that meet multiple constraints and traverse the candidate worker set to obtain the optimal worker without revealing the input and output. Rigorous security analysis proves that our EPTS scheme effectively preserves user privacy, single-dimensional privacy, and access pattern privacy. Extensive experimental results validate its superior efficiency compared with the state-of-the-art scheme.

The cloud-edge computing model has been expected to play a revolutionary role in promoting the quality of future generation large-scale Internet of Things (IoT) services. However, security and privacy in data sharing remain crucial issues hindering the success of cloud-edge IoT services. While some solutions based on attribute-based encryption (ABE) have been proposed to address these issues, they still face practical challenges such as attribute privacy leakage, resource-constrained devices, dynamic user groups, inflexible and inefficient service response. To address these challenges, this paper proposes a privacy-preserving fine-grained data sharing scheme with dynamic service (PF2DS), which implements access control by calculating the inner product between an attribute vector and an access vector. PF2DS is also capable of providing dynamic user group services through an efficient and indirect user revocation mechanism that periodically updates the key-embedded leaf nodes. Building on PF2DS, edge-assisted PF2DS (EPF2DS) delegates most of the operations to the edge device, which facilitates the performance of resource-constrained IoT devices. EPF2DS also supports efficient and asynchronous keyword search over the ciphertexts stored in the cloud. We demonstrate the security by the rigorous security proof. Both theoretical comparisons and experimental simulations demonstrate the practicality and superiority of our schemes over existing works.

The proliferation of intelligent connected vehicles (ICVs) has catalyzed the emergence of vehicular crowdsensing (VCS) applications, wherein sensing tasks are assigned to ICVs with abundant sensing resources and high mobility. To select workers whose future trajectories have sufficient spatio-temporal similarity with the target sensing area, workers unavoidably need to upload their trajectories to the VCS platform that is not fully trusted, thereby triggering location privacy concerns. Recently, numerous privacy-preserving worker selection schemes have been put forth. Nevertheless, they either fail to enable flexible arbitrary query ranges or incur substantial communication and computation costs, which severely limits their suitability for VCS applications. To tackle the above two issues simultaneously, we propose a novel efficient and privacy-preserving VCS worker selection scheme that supports flexible arbitrary spatial ranges. By utilizing the Bloom filter technique and lightweight cryptographic tools, our proposed scheme allows the VCS platform to efficiently collaborate with the fog server to compute the spatio-temporal similarity without leaking location-derived Bloom filters. Rigid security analysis shows that our scheme effectively preserves the location privacy of both workers and the query user. Extensive experiments are conducted and the results demonstrate that our scheme is significantly more efficient in both communication and computation compared with the state-of-the-art scheme.

Set similarity query is a fundamental query type in various applications, such as clinical diagnosis, online shopping, and mobile crowdsensing. Meanwhile, as the prevalence of outsourced query services, privacy-preserving set similarity query has been considerablely studied. However, to the best of our knowledge, most previously reported solutions suffer from applicability, efficiency, or security issues. Aiming at addressing these issues, we propose an efficient and privacy-preserving set similarity range query scheme (EPSet), where Jaccard similarity is employed as the similarity metric. Specifically, the set similarity range query is first transformed into multi-dimensional range queries by leveraging the triangle inequality of Jaccard distance. Then, a pivot-based k-d tree is designed for indexing the dataset and processing the set similarity query. After that, we design homomorphic encryption based privacy-preserving filter/refinement protocols, respectively named as PPF and PPR, to protect set similarity query privacy, and propose our EPSet scheme. The security of our scheme is proved under the simulation-based real/ideal model, and the performance is validated thorugh the extensive experiment evaluation.