Cryptographic Privacy Protection in Blockchain: Analysis of Strategies and Future Directions

Chen Lin

2024

Abstract

The growing concerns around data privacy have highlighted the need for effective privacy protection mechanisms in blockchain technology, making it a prominent research focus. This paper explores various privacy protection strategies in blockchain, particularly emphasizing the critical role of cryptographic techniques. This study examines the benefits and drawbacks of privacy-focused cryptographic methods, such as attribute-based encryption, homomorphic encryption, secure multiparty computing, and special data signatures, after analyzing advanced literature. The analysis shows that privacy issues in blockchain cannot be fully addressed at the application or contract layers alone; instead, a combination of cryptographic methods must be tailored to specific needs and scenarios. The paper further emphasizes that the practical implementation of these technologies should consider both timeliness and application relevance, carefully weighing the trade-offs between privacy and performance. In conclusion, the study highlights key challenges that must be resolved for blockchain privacy solutions to evolve, particularly in balancing transparency and confidentiality. This analysis provides insights into the future development of privacy protection in blockchain systems, urging for an integrated approach to leverage cryptographic innovations in real-world applications.

Paper Citation

in Harvard Style

Lin C. (2024). Cryptographic Privacy Protection in Blockchain: Analysis of Strategies and Future Directions. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 494-499. DOI: 10.5220/0013526900004619

in Bibtex Style

@conference{daml24,
author={Chen Lin},
title={Cryptographic Privacy Protection in Blockchain: Analysis of Strategies and Future Directions},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={494-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013526900004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Cryptographic Privacy Protection in Blockchain: Analysis of Strategies and Future Directions
SN - 978-989-758-754-2
AU - Lin C.
PY - 2024
SP - 494
EP - 499
DO - 10.5220/0013526900004619
PB - SciTePress