With the rapid growth of cloud computing, securely outsourcing sensitive data has become crucial for protecting user privacy. SQL, renowned for its expressiveness and flexibility, remains the predominant query language for big data analytics. However, existing access control mechanisms for encrypted SQL databases primarily operate at coarse granularity levels, such as databases, tables, or columns, failing to meet nuanced user access requirements at the individual data-element level. To address this challenge, we proposeEGuardSQL, a novel architecture that enables fine-grained, element-level access control over encrypted SQL tables. Our approach leverages Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to define hybrid access policies that integrate both row-level and column-level permissions, facilitating precise and flexible control. To mitigate the inherent computational overhead associated with CP-ABE, particularly when dealing with complex policy structures, we introduce an optimized reusable sub-policy mechanism that effectively reduces redundant cryptographic computations during encryption and decryption processes. We formally prove the selective IND-CPA security of the EGuardSQL scheme under the decisional$q$-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption and demonstrate its resistance to collusion attacks. Comprehensive performance evaluations illustrate that EGuardSQL achieves significant improvements in computational and communication efficiency compared to conventional CP-ABE approaches, while maintaining low storage overhead and robust security guarantees.

Intelligent transportation systems (ITS) involve data management and operation among multiple parties, making the trustworthiness and transparency of centralized data a highly challenging issue. Blockchain data storage, characterized by its immutability and multi-party collaboration, has emerged as a mainstream technology for trustworthy data sharing among multiple parties. However, the current on-chain data structures based on transactions, e.g., Ethereum’s MPT, struggle to address the scalability and efficient on-chain and off-chain collaboration required for data management with high spatio-temporal characteristics, as seen in intelligent transportation. In this paper, we propose a spatio-temporal scalable Merkle Patricia Tree (sMPT) structure for hierarchical organization of on-chain data objects (DOs), which is mapped to the off-chain transportation data entries through a DO packaging mechanism. The experimental evaluation results demonstrate the effectiveness of the proposed sMPT structure in basic functionality, like on-chain storage and sMPT compression. In terms of efficiency, sMPT outperforms the original MPT in retrieval and verification, particularly in batch verification.

Private data alignment, as the prerequisite for multiparty collaborative computation, attracts more attention in recent years, and some existing researches achieve the intersection sharing through two-party private set intersection (PSI) protocol based on various cryptographic techniques. However, they focus on the correctness and confidentiality of the protocol in the two-party scenario, while ignoring the efficiency and scalability in multiparty scenario. Additionally, the multiparty PSI protocol is difficult to be compatible with two parties simultaneously. To this end, we propose an oblivious pseudorandom function-based PSI scheme to achieve the data alignment, which is suitable for two parties and multiple parties. Specifically, to avoid frequent interactions among multiple parties, an efficient filtering algorithm is designed with the assistance of a server. The security proof for semi-honest and corrupted parties is provided, meanwhile, the computation and communication overhead analysis is given in detail. To evaluate the performance, we deploy the proposed scheme in two-party and multiparty scenario, and compare it with the existing protocols to discuss the execution complexity and overhead, which shows the efficiency and scalability of the proposed scheme in the multiparty scenario.

Private set intersection (PSI) has emerged as a key cryptographic protocol, enabling secure data sharing and facilitating collaborative computing among distributed data providers in recent years. However, it remains challenging to achieve efficient multiparty private set intersection (MPSI) for large-scale data and numerous participants in an open environment. To this end, we propose EL-MPSI, an Efficient and Lightweight MPSI scheme based on Vector Oblivious Linear Evaluation (VOLE) and Oblivious Key-Value Store (OKVS), which enables secure data sharing in settings with millions of datasets and dozens of participants. By simplifying the interaction process among multiple participants, the proposed scheme achieves constant-level round complexity and provides resistance against malicious adversaries, as well as collusion attack. Through theoretical analysis and experiments, we demonstrate that the security, efficiency and scalability of our scheme perform better than existing state-of-the-art (SOTA) works. For millions of datasets and dozens of participants, EL-MPSI achieves second-level latency while keeping client communication overhead to approximately 10 MB. Moreover, in scenarios of malicious adversary setting, the extra execution overhead is negligible, which effectively facilitates large-scale data sharing.

As car hire and sharing service is popular in the transportation market, secure driver identity verification is attracting more attention. However, the current verification mechanism focuses on performing authentication operations in the cloud server before drivers get access to the car, which results in potential privacy security issues. In this article, we present a privacy-aware architecture, MobiDIV, which is a client-only scheme, where all sensitive data are processed locally on the driver’s smartphone. To achieve real-time and robust driver identification during the driving life cycle, an efficient face feature extractor is proposed in MobiDIV. Specifically, two three-stream neural networks using the proposed efficient SqueezeNet structure are trained on our synthesized data set for different in-car uncertainties (pose, motion blur, nonalignment and low illumination). During authentication, only an adaptable embedding model is selected and conducted on phone for continuous feature extraction. The anomaly operation monitoring algorithm is then applied to the optical signal generated by phone flash for secure identity reidentification and verification failure message transmission. This allows us to further ensure the privacy of driver facial images without compromising on the real-time identity verification. We perform extensive experiments on various data sets. Compared to most SOTA deep neural networks on real-world open data sets, we achieve similar verification accuracy with fewer parameters and floating-point calculations. On the challenging synthetic test data sets, we even achieve a higher average verification accuracy. To assess the MobiDIV in-depth, the proposed model is integrated in car-sharing platform ICICV-E100 and the obtained results show the feasibility of our system.

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust has become increasingly apparent and received considerable scholarly attention in recent years. With the occurrence of various security incidents, data leakage accidents, and service fraud, which seriously affects the quality of service and system efficiency of IoT, trust is fast becoming a key issue in IoT system. In addition to the security and efficiency, trust further contains the reliability, attack resistance, fairness, flexibility, and incentive, resulting in a wide range of new researches from the trust management framework to the quantification method. In this work, various dimensions of trust, including definition, composition, aggregation, and computation are introduced and analyzed. We focus on comprehensive comparison of the state-of-the-art trust management researches and the related applications. Besides, we explore and discuss the important challenges, including performance bottleneck, bidirectional trust, dynamic changes of context, privacy preserving, and cross-domain issue. Some potential enabling technologies for trust management are further analyzed. The objective of this article is to comprehend the trust issue and the composition of trust management in IoT, and illustrate the difference of existing work, thereby motivating further research interest in this field.

Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

Load redistribution (LR) attacks have proven to be hard-detectable and damaging, which require effective corrective schemes to mitigate the impact on power grid operations. Traditional game-theoretic methods and corrective dispatches employing static line rating (SLR) have been studied for attack mitigation based on specific attack objectives but have high dispatch cost and limited performance of attack mitigation. This is because the power transfer capacity of the existing transmission network is underestimated with SLR, and in practical operations, the specific objective of the adversary is not available to the defender, which would introduce uncertainties to the design of corrective schemes. As such, this article incorporates the dynamic line rating (DLR) technology, which enhances the power transfer capability of the existing network, to develop the cost-effective corrective dispatch for mitigating LR attacks with unknown objectives. Specifically, a DLR-based robust corrective (DRC) dispatch model is presented, which guarantees the system security as well as the economic performance. A methodology utilizing the robust counterpart technique and column constraint generation (CCG) algorithm is proposed to solve the dispatch model in a decomposition framework. Case studies based on the IEEE 14- and 118-bus systems verify the performance of the proposed DRC dispatch in enhancing the cyber–physical security of power grids.

With the rapid increase of the industrial data and the development of the industrial Internet of Things (IIoT) paradigm, the efficiency and the quality of service of the emerging applications have been improved. However, the contradiction between data sharing and privacy preserving is still an obstacle in the IIoT. To this end, in this article, we propose a privacy-preserving and publicly auditable multiparty computation scheme for industrial data sharing and computing, which avoids privacy leakage and computation misbehavior by separating the data ownership, data use, and data verification. Using the blockchain technology, a transparent management platform is provided to recognize and trace the illegal data and computation behavior. Moreover, we integrate the noninteractive zero-knowledge proof in the multiparty interaction mechanism, wherein the verification of data consistency and computation validity is executed publicly on the blockchain. Finally, we implement experiment to evaluate the performance of the computation latency, communication overhead and the influence of encryption parameter, and the numerical results illustrate the efficiency and feasibility of our scheme.

Since the rising demand for dense estimation tasks in mobile applications, real-time semantic segmentation is increasingly desirable. Our work proposes a faster bilateral segmentation network (Faster BiSeNet) based on BiSeNetV2, which promotes feature fusion of the spatial and semantic branch with the more compact structure to improve real-time performance. Our light-weight design enhances the mutual connection between two branches in the shallow and deep layers by 1) Shallow feature sharing: using the simple linear operation to transform shallow features of the spatial branch as those needed in the semantic branch; 2) Deep feature aggregation: introducing a Gated Guided Aggregation Layer to guide appropriate spatial information to supplement the missing details of the semantic branch through gating mechanism, which avoids using unnecessary convolutions to select the aggregated features; 3) Auxiliary edge loss: making the gated output of the spatial branch focus on important boundary-related information, which facilitates the fusion of valuable edge information into the predicted results. Extensive experiments demonstrate that our approach obtains better realtime performance against a few state-of-the-art real-time semantic segmentation methods. Compared with BiSeNetV2, Faster BiSeNet respectively achieves 72.8%$(\uparrow 0.2\%)$I, 74.5%$(\uparrow 2.1\%)$) Mean IoU at the inference of 187 FPS$(\uparrow 31$FPS), 154 FPS$(\uparrow 29.5$FPS) on the challenging Cityscapes test set and CamVid validation set on one NVIDIA GeForce GTX 1080Ti card. This demonstrates that our method achieves the competitive segmentation performance at real-time speed.

Neural network pruning plays an important role in the deployment on resource-constrained devices by reducing the scale of the network and the computational complexity. However, existing pruning methods only consider the richness of information filters contain, without considering the distribution of information. In image classification, the information related to target area is very important. To address these limitations, we propose HCov to prune filters generating low covariance feature maps. The principle behind is that most of the feature maps generated by filters contain target area information, therefore, maps with low covariance contain either very little information or messy background information unrelated to target. Thus filters generating low covariance feature maps can be pruned with little accuracy drop. HCov calculates the covariance between feature maps in the same layer and removes filters with low covariance feature maps. Through experiments on single-branch and multi-branch networks, the results prove that HCov can prune more redundant filters while maintaining better accuracy. Notably, our method can reduce 68.6% parameters and 71.7% FLOPs of ResNet-110 with only 0.26% top-1 accuracy loss on CIFAR-10. With ResNet-50, we achieve a 44.7% FLOPs reduction by removing 40.8% of the parameters, with only a loss of 0.62% in the top-1 accuracy on ImageNet, which has advanced the state-of-the-art.

Social Internet of Things (SIoT) an emerging Internet-of-Things (IoT) service infrastructure, which integrates the social concept into IoT systems for enhancing service efficiency. By establishing a social relationship among objects, IoT devices can autonomously interact with each other free from human intervention. One critical issue in the development of SIoT is the trust issue, which is essential in fostering cooperation among objects. The existing approaches largely fall short in trust quantification, and also suffer from the bias of trustworthiness evaluation without properly considering inherently dynamic context changes and potential malicious behaviors. In this work, we combine social trust theory and incorporate the unique characteristics of IoT devices to address the trust issue in SIoT. We establish a general trust model which comprehensively captures the competence, willingness, and social relationship in SIoT. Specifically, we define two functions in terms of the Degree of Importance (DoI) and the Degree of Contribution (DoC) to compute the competence and willingness, and we present a formal quantitative trust model that is robust in the dynamic environment and against common malicious attacks. The effectiveness of our proposed trust model is verified through the security analysis and a series of simulation experiments. The results demonstrate that the proposed trust model is reliable and efficient in promoting the success rate of services, as well as improving the efficiency and security of services in SIoT.

The emerging mobile-edge computing paradigm provides opportunities for the resource-hungry mobile devices (MDs) to migrate computation. In order to satisfy the requirements of MDs in terms of latency and energy consumption, recent researches proposed diverse computation offloading schemes. However, they either fail to consider the potential computing resources at the edge, or ignore the selfish behavior of users and the dynamic resource adaptability. To this end, we study the computation offloading problem and take into consideration the dynamic available resource of idle devices and the selfish behavior of users. Furthermore, we propose a game theoretic offloading method by regarding the computation offloading process as a resource contention game, which minimizes the individual task execution cost and the system overhead. Utilizing the potential game, we prove the existence of Nash equilibrium (NE), and give a lightweight algorithm to help the game reach a NE, wherein each user can find an optimal offloading strategy based on three contention principles. Additionally, we conduct analysis of computational complexity and the Price of Anarchy (PoA), and deploy three baseline methods to compare with our proposed scheme. Numerical results illustrate that our scheme can provide high-quality services to users, and also demonstrate the effectiveness, scalability and dynamic resource adaptability of our proposed algorithm in a multiuser network.

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual tracking. DCFs utilizing a periodic assumption of the samples to train a correlation filter performs efficiently in tracking. However, the DCF framework is still an open-loop system prone to tracking failures caused by model drift. In this paper, we present a Spatial-Corrected Regularized Correlation Filters (SCRCF) which is a DCF-based tracker with a correction mechanism. SCRCF exploit the advantages of the spatial regularization method to design a corrective feedback mechanism. The mechanism calculates the reasonable offset to influence the spatial regularization coefficients by observing the output responses of multiple trackers, so as to correct the unreasonable tracking results. Compared to conventional DCF-based trackers, SCRCF is more robust to handle some complicated tracking scenes, such as occlusion and motion blur. Extensive experiments on OTB-2015 benchmarks demonstrate our tracker outperforms most state-of-the-art trackers.

This work investigates energy-optimal motion planning for a class of multiaxis motion systems where the system dynamics are linear time-invariant and decoupled in each axis. Solving the problem in a reliable and efficient manner remains challenging owing to the presence of various constraints on control and states, nonconvexity in its cost function, and obstacles. This paper shows how the cost function can be convexified by considering the system dynamics, while decomposing decision variables to obtain a convex representation of collision avoidance constraints. With the convexified cost function and constraints, the original problem is decomposed into two quadratic programming (QP) problems. An alternating quadratic programming (AQP) algorithm is proposed to solve both the QP problems alternatingly and iteratively till convergence. Requiring an initial feasible trajectory as a guess, AQP necessarily converges to an energy-efficient solution that is homotopic to the initial guess. Under certain circumstances, AQP is guaranteed to produce a local optimum. Simulation demonstrates that AQP is computationally efficient and reliable while claiming comparable energy saving as the mixed-integer QP approach.

With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply-demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.

As physical obstacles, vehicles have a significant impact on the efficient propagation of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS link between the source and receivers. Obstructing effect of vehicles will diminish the effective coverage of broadcast as part of vehicles can not decode the broadcast successfully and incur severe impact on road safety. However, this impact has not been addressed by existing protocols. In this paper, we first present the definition of broadcast efficiency (BE) as the metric for measuring the obstructing effect and conclude the optimization issue of propagation. We further propose a distributed algorithm for mitigating this impact that can be embedded in VANETs. This algorithm maximizes the effective coverage of the broadcast of safety-related information and meanwhile satisfies the constraints on both link state and delay by selecting relay according to BE and referential propagation distance. Simulation results demonstrate that the proposed algorithm promotes the efficiency of propagation in dynamic multilane VANETs with varying vehicular distribution.

Smartphone is widely used in indoor map construction with its build-in sensors. However, the low accuracy problem of build-in sensors always causes the collected user trajectories noisy. The significant problem we face is the low accuracy of walk heading estimated by build-in sensors because of phone heading fluctuation and magnetic field anomaly. This paper presents HiHeading - a high reliable crowdsourcing-based indoor map construction system. HiHeading leverages build-in inertial sensors to construct accurate motion traces. These traces are generated by HiHeading with high accuracy based on the novel ideal of fusing gyroscope and orientation sensor to get reliable walk heading estimation in indoor dead reckoning (DR). To evaluate our system, we have tested it in a middle size indoor office by recording 3 people's walk trajectories during 5 days. We present an evaluation of our system and the experiment result shows 70% of the heading estimation error is lower than 10 degrees.

Given the explosive growth of cameras and LCDs in todays society, cameras get easy access to nearby LCDs and could be utilized for communication access based on visible light communication (VLC). However, there is still no prototype to implement two-way communications. Main challenge lies in the reflection noise since two screens of LCD and/or smartphone are in face-to-face position for two-way communications. In this paper, we advance a step for developing MLCS, a face-to-face mobile-LCD communication system using off-the-shelf camera-mounted LCD and smartphone. More specifically, we propose ADIC, an ADjacent Image Calibration approach to eliminate the reflection noise. ADIC exploits our experimental observation that black-screen image can capture the main reflection noise. To increase the throughput, ADIC presents an adaptive black-screen image calibration method based on the feedback information for characterizing the reflection index of received image. Our approach is evaluated through extensive experiments on smartphone and laptop.

Embedded smart cameras have made a dramatic shift towards distributed surveillance systems by combining sensing, processing and communicating on a single platform. A critical issue in embedded smart cameras is resource-limited, which poses great challenging in designing fast and efficient vision algorithms. In this paper, we explore light-weighted foreground detection in resource-limited embedded smart cameras. More specifically, we propose RaFFD (Resource-aware Fast Foreground Detection) that reduces the computation and storage overhead in foreground detection. Observing that computation and storage overhead increase proportionally to its pixel manipulation, RaFFD deals with the target's contour points instead of the whole image. RaFFD incorporates a contour-based detection with dynamic background update, ensuring accurate foreground detection and address the bottlenecks of processing speed. We have implemented RaFFD on the our embedded smart camera platform based on CITRIC architecture. Our experimental evaluation shows that RaFFD can detect foreground with close to 95% accuracy and 6% false alarm. Even in an challenging scenario with illumination and vibration influence, RaFFD can still maintain the good robustness. Compared to the recently detection method oriented to embedded systems, RaFFD can increase processing speed to approximately twice and decrease memory consumption by 68%.

Distributed camera networks represent an emerging trend for the incorporation of pan-tilt-zoom (PTZ) cameras in next generation large-scale wireless surveillance systems. The adoption of PTZ cameras with dynamic field of view nature can increase the resolution of mobile targets and adapt the network coverage significantly. However, leverage these benefits calls for more configuration cost and sophisticated solutions. In this paper, we consider the specific problem of coverage problem in terms of configuration cost and coverage quality in PTZ camera networks. We argue that the coverage algorithms can exploit the dynamic field of view nature of PTZ cameras to reduce the configuration cost by taking into account the heterogeneous coverage quality demands for different targets in a surveillance application. We propose a new coverage definition, termed probability coverage (p-coverage), according to the patrolling property of PTZ cameras. A optimal coverage issues, min-quantity, is formulated underlying the proposed p-coverage framework. A greedy-based heuristic algorithm is proposed to solve the above probability-based optimal coverage problem. The proposed solutions are evaluated to highlight the relative significance of configuration cost gain with respect to diverse coverage demand.

Dynamic spectrum auction (DSA) is a promising market mechanism to stimulate primary users (PUs) to lease their idle spectrum resources to improve their revenue. In this work, we focus on dynamic spectrum auction design which considers both space and time properties of spectrum. We propose a dynamic spectrum auction platform, where secondary users (SUs)enter a spectrum trading stochastically and spectrum platform determines the market clearing time according to its observations. We solve the problem of determining when to give market clearing to generate optimal revenue for PUs. Optimal market clearing algorithms are developed under both geographical partitioning approaches and conflict graph solutions, which set a benchmark for future dynamic spectrum auction market.