Security Challenges and Performance Trade-Offs in On-Chain and Off-Chain Blockchain Storage: A Comprehensive Review

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3225 - 3225

Published: March 15, 2025

Blockchain-based data storage methods offer strong integrity, decentralized security, and transparent access control but also face scalability challenges, high computational costs, complex management. This study provides a comprehensive review of on-chain, off-chain, hybrid architectures, analyzing their security vulnerabilities, performance trade-offs, industry-specific applications. On-chain ensures immutability, by storing directly on the blockchain; however, it is associated with transaction costs limitations. In contrast, off-chain solutions reduce enhance outside blockchain introduce risks related to integrity in environments. Hybrid approaches aim balance cost, integrating strengths both on-chain solutions. examines fundamental components blockchain-based systems, sector-specific applications, technical challenges they present. Additionally, explores trade-offs between decentralization, offering insights into optimization strategies. As result, this evaluates protocols, efficiency sustainability distributed solutions, contributing future research field.

Language: Английский

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hChain 2.0: Leveraging Blockchain and Distributed File System for EHR Management in Smart Healthcare

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Jan. 28, 2025

Language: Английский

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Ultra-Lightweight Cryptographic Algorithm for Resource Constraints Medical Iot Devices to Enhance Healthcare Security

Published: Jan. 1, 2025

Language: Английский

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A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 244 - 244

Published: March 18, 2025

Federated learning (FL) is a machine technique where clients exchange only local model updates with central server that combines them to create global after training. While FL offers privacy benefits through training, privacy-preserving strategies are needed since can leak training data information due various attacks. To enhance and attack robustness, techniques like homomorphic encryption (HE), Secure Multi-Party Computation (SMPC), the Private Aggregation of Teacher Ensembles (PATE) be combined FL. Currently, no study has more than two or comparatively analyzed their combinations. We conducted comparative in FL, analyzing performance security. implemented using an artificial neural network (ANN) Malware Dataset from Kaggle for malware detection. privacy, we proposed models combining PATE, SMPC, HE. All were evaluated against poisoning attacks (targeted untargeted), backdoor attack, inversion man middle attack. The maintained while improving robustness. FL_SMPC, FL_CKKS, FL_CKKS_SMPC improved both resistance. outperformed base FL_PATE_CKKS_SMPC achieved lowest success rate (0.0920). best resisted untargeted (0.0010 rate). FL_CKKS defended targeted (0.0020 FL_PATE_SMPC (19.267 MSE). degradation accuracy (1.68%), precision (1.94%), recall F1-score (1.64%).

Language: Английский

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A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses

Deleted Journal, Journal Year: 2025, Volume and Issue: 22(1)

Published: April 5, 2025

Language: Английский

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Safeguarding the Internet of Health Things: advancements, challenges, and trust-based solution

Telecommunication Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Language: Английский

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