Efficient and Secure Cross-Domain Data-Sharing for Resource-Constrained Internet of Things
- URL: http://arxiv.org/abs/2411.09229v1
- Date: Thu, 14 Nov 2024 06:53:03 GMT
- Title: Efficient and Secure Cross-Domain Data-Sharing for Resource-Constrained Internet of Things
- Authors: Kexian Liu, Jianfeng Guan, Xiaolong Hu, Jianli Liu, Hongke Zhang,
- Abstract summary: We propose an efficient, secure blockchain-based data-sharing scheme for the Internet of Things.
First, our scheme adopts a distributed key generation method, which avoids single point of failure.
Also, the scheme provides a complete data-sharing process, covering data uploading, storage, and sharing, while ensuring data traceability, integrity, and privacy.
- Score: 2.5284780091135994
- License:
- Abstract: The growing complexity of Internet of Things (IoT) environments, particularly in cross-domain data sharing, presents significant security challenges. Existing data-sharing schemes often rely on computationally expensive cryptographic operations and centralized key management, limiting their effectiveness for resource-constrained devices. To address these issues, we propose an efficient, secure blockchain-based data-sharing scheme. First, our scheme adopts a distributed key generation method, which avoids single point of failure. This method also allows independent pseudonym generation and key updates, enhancing authentication flexibility while reducing computational overhead. Additionally, the scheme provides a complete data-sharing process, covering data uploading, storage, and sharing, while ensuring data traceability, integrity, and privacy. Security analysis shows that the proposed scheme is theoretically secure and resistant to various attacks, while performance evaluations demonstrate lower computational and communication overhead compared to existing solutions, making it both secure and efficient for IoT applications.
Related papers
- Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC)
ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic.
We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions.
This paper proposes a digital twin (DT) and federated digital twin (FL) scheme.
The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
arXiv Detail & Related papers (2024-11-04T17:48:02Z) - Secure Computation and Trustless Data Intermediaries in Data Spaces [0.44998333629984877]
This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces.
We exploit the introduced secure methods, i.e. Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE)
We present solutions through real-world use cases, including air traffic management, manufacturing, and secondary data use.
arXiv Detail & Related papers (2024-10-21T19:10:53Z) - ZK-DPPS: A Zero-Knowledge Decentralised Data Sharing and Processing Middleware [3.2995127573095484]
We propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs.
Privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction.
We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario.
arXiv Detail & Related papers (2024-10-21T01:23:37Z) - Complete Security and Privacy for AI Inference in Decentralized Systems [14.526663289437584]
Large models are crucial for tasks like diagnosing diseases but tend to be delicate and not very scalable.
Nesa solves these challenges with a comprehensive framework using multiple techniques to protect data and model outputs.
Nesa's state-of-the-art proofs and principles demonstrate the framework's effectiveness.
arXiv Detail & Related papers (2024-07-28T05:09:17Z) - Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks [20.864048794953664]
This paper proposes a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles (IoVs) with limited computational resources.
We first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing.
We develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data.
arXiv Detail & Related papers (2024-07-26T04:19:37Z) - Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications.
Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs.
We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
arXiv Detail & Related papers (2024-03-29T14:58:28Z) - Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks.
In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
arXiv Detail & Related papers (2024-03-29T12:01:31Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing.
Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
arXiv Detail & Related papers (2022-05-08T14:38:23Z) - Efficient Logistic Regression with Local Differential Privacy [0.0]
Internet of Things devices are expanding rapidly and generating huge amount of data.
There is an increasing need to explore data collected from these devices.
Collaborative learning provides a strategic solution for the Internet of Things settings but also raises public concern over data privacy.
arXiv Detail & Related papers (2022-02-05T22:44:03Z)
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.