Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes ofte...
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Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.
In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The s...
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Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concern...
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Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure, quantum-resistant blind signatures. In this paper, we introduce lattice-based forward-secure blind signature (LFSBS), a lattice-based forward-secure blind signature scheme for medical privacy preservation in BIoMT. LFSBS achieves forward security by constructing a key evolution mechanism using a binary tree structure. This mechanism ensures that even if future encryption keys are leaked, past data can still remain secure. Meanwhile, LFSBS realizes post-quantum security based on the hardness assumption of small integer solution (SIS), making it resistant to potential quantum computing attacks. In addition, we formally define and prove the security of LFSBS in a random oracle model, including blindness and forward-secure unforgeability. Comprehensive performance evaluation shows that LFSBS performs well in terms of computational overhead, with a reduction of 22%–73% compared to previous schemes.
With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has ...
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Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC)...
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Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.
Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive com...
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Space-Air-Ground Integrated networks (SAGINs) are important infrastructures of 6G. With the support of SAGINs, data collection in unattended areas becomes possible. However, data measured by various devices in unatten...
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With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effect...
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Device activity detection and channel estimation are central problems in massive Machine Type Communication (mMTC) scenarios. Given the sparse distribution of active devices, compressive sensing (CS) emerges as a viab...
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This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vi...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.
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