1,307 research outputs found

    Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

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    Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

    Smart Road Danger Detection and Warning

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    Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information. To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy. Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. The EcRD is proposed to improve latency and reduce labeling cost, which is an Edge-cloud-based Road Damage detection and warning framework that leverages the fast-responding advantage of edges and the large storage and computation resources advantages of the cloud. In EcRD, a simple yet efficient road segmentation algorithm is introduced for fast and accurate road area detection by filtering out noisy backgrounds. Additionally, a light-weighted road damage detector is developed based on Gray Level Co-occurrence Matrix (GLCM) features on edges for rapid hazardous road damage detection and warning. Further, a multi-types road damage detection model is proposed for long-term road management on the cloud, embedded with a novel image-label generator based on Cycle-Consistent Adversarial Networks, which automatically generates images with corresponding labels to improve road damage detection accuracy further. EcRD achieves 91.96% accuracy with only 0.0043s latency, which is around 579 times faster than cloud-based approaches without affecting users' experience while requiring very low storage and labeling cost. Secondly, although EcRD relieves the problem of high latency by edge computing techniques, road users can only achieve warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' personal information. A novel FedRD named FedRD is developed to improve the coverage range of warning information and protect data privacy. In FedRD, a new hazardous road damage detection model is proposed leveraging the advantages of feature fusion. A novel adaptive federated learning strategy is designed for high-performance model learning from different edges. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results show that FedRD achieves similar high detection performance (i.e., 90.32% accuracy) but with more than 1000 times wider coverage than the state-of-the-art, and works well when some edges only have limited samples; besides, it largely preserves users' privacy. Finally, despite the success of EcRD and FedRD in improving latency and protecting privacy, they are only based on a single modality (i.e., image/video) while nowadays, different modalities data becomes ubiquitous. Also, the communication cost of EcRD and FedRD are very high due to undifferentiated data transmission (both normal and dangerous data) and frequent model exchanges in its federated learning setting, respectively. A novel edge-cloud-based privacy-preserving Federated Multimodal learning framework for Road Danger detection and warning named FedMRD is introduced to leverage the multi-modality data in the real-world and reduce communication costs. In FedMRD, a novel multimodal road danger detection model considering both inter-and intra-class relations is developed. A communication-efficient federated learning strategy is proposed for collaborative model learning from edges with non-iid and imbalanced data. Further, a new multimodal differential privacy technique for high dimensional multimodal data with multiple attributes is introduced to protect data privacy directly on users' devices before uploading to edges. Experimental results demonstrate that FedMRD achieves around 96.42% higher accuracy with only 0.0351s latency and up to 250 times less communication cost compared with the state-of-the-art, and enables collaborative learning from multiple edges with non-iid and imbalanced data in different modalities while preservers users' privacy.2021-11-2

    Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

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    open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture
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