A lightweight blockchain-based access control scheme for integrated edge
computing in the internet of things
- URL: http://arxiv.org/abs/2111.06544v2
- Date: Wed, 17 Nov 2021 02:36:32 GMT
- Title: A lightweight blockchain-based access control scheme for integrated edge
computing in the internet of things
- Authors: Jie Zhang, Lingyun Yuan and Shanshan Xu
- Abstract summary: We propose an attribute-based encryption and access control scheme (ABE-ACS) for the Edge-Iot network.
For the problems of high resource consumption and difficult deployment of existing blockchain platforms, we design a lightweight blockchain (LBC)
Six smart contracts are designed to realize the ABAC and penalty mechanism, with which ABE is outsourced to edge nodes for privacy and integrity.
- Score: 4.308257382729074
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In view of the security issues of the Internet of Things (IoT), considered
better combining edge computing and blockchain with the IoT, integrating
attribute-based encryption (ABE) and attribute-based access control (ABAC)
models with attributes as the entry point, an attribute-based encryption and
access control scheme (ABE-ACS) has been proposed. Facing Edge-Iot, which is a
heterogeneous network composed of most resource-limited IoT devices and some
nodes with higher computing power. For the problems of high resource
consumption and difficult deployment of existing blockchain platforms, we
design a lightweight blockchain (LBC) with improvement of the proof-of-work
consensus. For the access control policies, the threshold tree and LSSS are
used for conversion and assignment, stored in the blockchain to protect the
privacy of the policy. For device and data, six smart contracts are designed to
realize the ABAC and penalty mechanism, with which ABE is outsourced to edge
nodes for privacy and integrity. Thus, our scheme realizing Edge-Iot privacy
protection, data and device controlled access. The security analysis shows that
the proposed scheme is secure and the experimental results show that our LBC
has higher throughput and lower resources consumption, the cost of encryption
and decryption of our scheme is desirable.
Related papers
- FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios [0.0]
This study proposes an advanced Learning (FL) framework designed to enhance data privacy and security in IoT environments.
We integrate Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC) and technology.
Unlike traditional FL, our framework enables secure, decentralized authentication and encryption directly on IoT devices.
arXiv Detail & Related papers (2024-10-26T19:30:53Z) - Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach [20.821562115822182]
This paper introduces a novel OR-aggregation approach for zero-knowledge set membership proofs.
We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis.
Results show significant improvements in proof size, generation time, and verification efficiency.
arXiv Detail & Related papers (2024-10-11T18:16:34Z) - FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System [11.901770945295391]
We propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS.
Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness.
arXiv Detail & Related papers (2024-06-06T02:23:12Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Security and Privacy Enhancing in Blockchain-based IoT Environments via Anonym Auditing [0.0]
We propose a novel framework that combines the decentralized nature of blockchain with advanced security protocols tailored for IoT contexts.
We outline the architecture of blockchain in IoT environments, emphasizing the workflow and specific security mechanisms employed.
We introduce a security protocol that integrates privacy-enhancing tools and anonymous auditing methods, including the use of advanced cryptographic techniques for anonymity.
arXiv Detail & Related papers (2024-03-03T01:09:43Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - Quantum Resistant Ciphertext-Policy Attribute-Based Encryption Scheme with Flexible Access Structure [0.0]
We present a novel ciphertext-policy based encryption (CP-ABE) scheme that offers a flexible access structure.
Our scheme incorporates an access tree as its access control policy, enabling fine-grained access control over encrypted data.
The security of our scheme is provable under the hardness assumption of the decisional Ring-Learning with Errors (R-LWE) problem.
arXiv Detail & Related papers (2024-01-25T10:55:23Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A
Reinforcement Learning Based Approach [61.74489383629319]
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests.
Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
arXiv Detail & Related papers (2023-03-05T12:25:49Z) - Privacy-Preserving Joint Edge Association and Power Optimization for the
Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
We investigate the privacy-preserving joint edge association and power allocation problem.
The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
arXiv Detail & Related papers (2023-01-26T10:09:23Z) - Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications.
We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology.
We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
arXiv Detail & Related papers (2021-06-03T16:45:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.