Overprivilege Attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-grained access control and strong auditability for IoT. TBAC materializes the virtual access power into a definite-amount, secure and accountable cryptographic coin, termed “tokoin” (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. A tokoin carries a fine-grained policy defined by the resource owner to specify the requirements to be satisfied before an access is granted, and the behavioral constraints that describe the correct procedure to follow during access. The strong-auditability is achieved with blockchain and a TEE-enabled trusted access control object (TACO) to ensure that all access activities are securely monitored and auditable. We prototype TBAC by implementing all its functions with well-studied cryptographic primitives over different blockchain platforms, building a TACO on top of the ARM Cortex-M33 TEE microcontroller, and constructing a user-friendly APP for regular users. A case study is finally presented to demonstrate how TBAC is employed to enable autonomous and secure in-home cargo delivery.

Graphs are widely employed in complex system modeling, VLSI design, and social analysis. Mining cohesive subgraphs is a fundamental problem in graph analysis, while implementing cohesive subgraphs requires analysts to not only ensure cohesiveness but also consider the computational intractability. Among a variety of diverse cohesive structures, k-truss exhibits a perfect trade-off between structure tightness and computational efficiency. In a k-truss, each edge is present in at least k-2 triangles. This study aims to contribute to this growing area of truss maintenance in fully dynamic graphs by avoiding expensive re-computation. Specifically, we consider the challenging scenario of batch processing of edge and vertex insertion/deletion and propose efficient algorithms that can maintain the trusses by only searching a very small range of affected edges. Also, our algorithms allow parallel implementations to further improve the efficiency of maintenance. Extensive experiments on both real-world static and temporal graphs illustrate the efficiency and scalability of our algorithms.

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as$n$-Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.

We consider a$K$-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. Although recent studies show the power of collaboration among the agents in improving the efficacy of learning, it is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

The proliferation of edge computing brings new challenges due to the complexity of decentralized edge networks. Software-defined networking (SDN) takes advantage of pro-grammability and flexibility in handling complicated networks. However, it remains a problem of designing a both trusted and scalable SDN control plane, which is the core component of the SDN architecture for edge computing. In this paper, we propose Curb, a novel group-based SDN control plane that seamlessly integrates blockchain and BFT consensus to ensure byzantine fault tolerance, verifiability, traceability, and scalability within one framework. Curb supports trusted flow rule updates and adaptive controller reassignment. Importantly, we leverage a group-based control plane to realize a scalable network where the message complexity of each round is upper bounded by O(N), where N is the number of controllers, to reduce overheads caused by blockchain consensus. Finally, we conduct extensive simulations on the classical Internet2 network to validate our design.

Vehicle reidentification, aiming at identifying vehicles across images, has drawn a lot of attention and has made significant achievements in recent years. However, vehicle reidentification remains a challenging task caused by severe appearance changes due to different orientations. In practice, the result of reidentification is greatly influenced by the pose of vehicles, and we call this influence as a pose barrier problem. One way to address the pose barrier problem is to train a feature representation that is invariant for various vehicle poses. To this end, we present pose robust features (PRFs) that contains two components: 1) pose-invariant features (PIFs) and 2) pose discriminative features (PDFs). On the one hand, PIF is the expert in exploring the overall characteristic of vehicles. When training PIF, we adopt an identity classifier as well as an orientation classifier. In addition, an adversarial loss is deployed in the PIF network. On the other hand, we design a PDF network, which has a similar architecture to the PIF network but can distinguish the difference between local details. The difference between PDF and PIF is that the network of training PDF does not apply the adversarial loss. Finally, by combining PIF and PDF, PRF has the advantages of the two features and can alleviate the influence of the pose barrier problem. Experiments are conducted on the VeRi-776 and VehicleID data sets. We show that PIF and PDF are complementary and that PRF produces competitive performance compared with state-of-the-art approaches.

Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.

Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.

Data association is one of the key research in tracking-by-detection framework. Due to frequent interactions among targets, there are various relationships among trajectories in crowded scenes which leads to problems in data association, such as association ambiguity, association omission, etc. To handle these problems, we propose hypothesis-testing based tracking (HTBT) framework to build potential associations between target by constructing and testing hypotheses. In addition, a spatio-temporal interaction graph (STIG) model is introduced to describe the basic interaction patterns of trajectories and test the potential hypotheses. Based on network flow optimization, we formulate offline tracking as a MAP problem. Experimental results show that our tracking framework improves the robustness of tracklet association when detection failure occurs during tracking. On the public MOT16, MOT17 and MOT20 benchmark, our method achieves competitive results compared with other state-of-the-art methods.

Recently, most multiple object tracking (MOT) algorithms adopt the idea of tracking-by-detection. Relevant research shows that the performance of the detector obviously affects the tracker, while the improvement of detector is gradually slowing down in recent years. Therefore, trackers using tracklet (short trajectory) are proposed to generate more complete trajectories. Although there are various tracklet generation algorithms, the fragmentation problem still often occurs in crowded scenes. In this paper, we introduce an iterative clustering method that generates more tracklets while maintaining high confidence. Our method shows robust performance on avoiding internal identity switch. Then we propose a deep association method for tracklet association. In terms of motion and appearance, we construct motion evaluation network (MEN) and appearance evaluation network (AEN) to learn long-term features of tracklets for association. In order to explore more robust features of tracklets, a tracklet-based training mechanism is also introduced. Tracklet groups are used as the input of the networks instead of discrete detections. Experimental results show that our training method enhances the performance of the networks. In addition, our tracking framework generates more complete trajectories while maintaining the unique identity of each target as the same time. On the latest MOT 2017 benchmark, we achieve state-of-the-art results.