Towards a Novel Privacy-Preserving Distributed Multiparty Data Outsourcing Scheme for Cloud Computing with Quantum Key Distribution
- URL: http://arxiv.org/abs/2407.18923v1
- Date: Tue, 9 Jul 2024 15:53:04 GMT
- Title: Towards a Novel Privacy-Preserving Distributed Multiparty Data Outsourcing Scheme for Cloud Computing with Quantum Key Distribution
- Authors: D. Dhinakaran, D. Selvaraj, N. Dharini, S. Edwin Raja, C. Sakthi Lakshmi Priya,
- Abstract summary: This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs)
We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment.
The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments.
Related papers
- Quantum delegated and federated learning via quantum homomorphic encryption [0.5939164722752263]
We present a general framework that enables quantum delegated and federated learning with atheoretical data privacy guarantee.
We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing.
arXiv Detail & Related papers (2024-09-28T14:13:50Z) - Cybersecurity in the Quantum Era: Assessing the Impact of Quantum Computing on Infrastructure [0.04096453902709291]
This analysis explores the impact of quantum computing on critical infrastructure and cloud services.
We advocate for proactive security strategies and collaboration between sectors to develop and implement quantum-resistant cryptography.
This blueprint strengthens each area's defenses against potential quantum-induced cyber threats.
arXiv Detail & Related papers (2024-04-16T15:36:23Z) - 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) - Neural auto-designer for enhanced quantum kernels [59.616404192966016]
We present a data-driven approach that automates the design of problem-specific quantum feature maps.
Our work highlights the substantial role of deep learning in advancing quantum machine learning.
arXiv Detail & Related papers (2024-01-20T03:11:59Z) - The Evolution of Quantum Secure Direct Communication: On the Road to the
Qinternet [49.8449750761258]
Quantum secure direct communication (QSDC) is provably secure and overcomes the threat of quantum computing.
We will detail the associated point-to-point communication protocols and show how information is protected and transmitted.
arXiv Detail & Related papers (2023-11-23T12:40:47Z) - Designing Hash and Encryption Engines using Quantum Computing [2.348041867134616]
We explore quantum-based hash functions and encryption to fortify data security.
The integration of quantum and classical methods demonstrates potential in securing data in the era of quantum computing.
arXiv Detail & Related papers (2023-10-26T14:49:51Z) - From Portfolio Optimization to Quantum Blockchain and Security: A
Systematic Review of Quantum Computing in Finance [0.0]
We provide an overview of the recent work in the quantum finance realm from various perspectives.
The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation.
We give a comprehensive overview of the applications of quantum computing in the field of blockchain technology.
arXiv Detail & Related papers (2023-06-12T19:53:23Z) - Deploying hybrid quantum-secured infrastructure for applications: When
quantum and post-quantum can work together [0.8702432681310401]
Quantum key distribution is secure against unforeseen technological developments.
Post-quantum cryptography is believed to be secure even against attacks with both classical and quantum computing technologies.
Various directions in the further development of the full-stack quantum-secured infrastructure are also indicated.
arXiv Detail & Related papers (2023-04-10T13:44:21Z) - When Quantum Information Technologies Meet Blockchain in Web 3.0 [86.91054991998273]
We introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.
We discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0.
arXiv Detail & Related papers (2022-11-29T05:38:42Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - Backflash Light as a Security Vulnerability in Quantum Key Distribution
Systems [77.34726150561087]
We review the security vulnerabilities of quantum key distribution (QKD) systems.
We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks.
arXiv Detail & Related papers (2020-03-23T18:23:12Z)
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.