Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing

The sharing of DNA sequence data is essential for advancing medical technology and fostering innovation in healthcare. However, DNA sequences encode sensitive information, such as gender, physical attributes, and genetic predispositions, necessitating stringent privacy safeguards. Existing data-sharing frameworks often fail to adequately address these privacy concerns. To overcome these challenges, this study proposes a blockchain-based, privacy-preserving ecosystem for DNA sequence sharing. The system employs a decentralized architecture to manage digital identities and access permissions, ensuring robust privacy and data security. Smart contract functionalities allow users to assign granular access controls to specific DNA sequence segments, enabling selective sharing with trusted recipients. Furthermore, research institutions are required to obtain certification and classification from governmental authorities, enhancing trust and system reliability. The user-centric design prioritizes privacy, security, and autonomy, simplifying operational processes and fostering user trust. By incentivizing DNA data sharing, the proposed model aims to accelerate medical advancements while maintaining stringent privacy protections, establishing a secure and scalable ecosystem for DNA sequence sharing. Experimental results from a prototype implementation indicate that the system achieves a throughput of up to 10–20 transactions per second for identity and access operations while incurring acceptable on-chain costs (≈1.3 million gas to deploy contracts and 400–800 k gas per user registration). These performance metrics underscore the feasibility and efficiency of the proposed approach.

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Publication Details

Journal
Applied Sciences
Published
2025-03-14
DOI
https://doi.org/10.3390/app15063193
Citations
3
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
7.44

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article

Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing

Ching-Hsi Tseng, Thi-Thanh-An Nguyen, Yuchen Lin, Shyan‐Ming Yuan et al.
3 citations
Applied Sciences
Privacy-Preserving Technologies in Data
7.44
article

Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing

Ching-Hsi Tseng, Thi-Thanh-An Nguyen, Yuchen Lin, Shyan‐Ming Yuan, Yu-Heng Hsieh
article en
3 citations

Abstract

The sharing of DNA sequence data is essential for advancing medical technology and fostering innovation in healthcare. However, DNA sequences encode sensitive information, such as gender, physical attributes, and genetic predispositions, necessitating stringent privacy safeguards. Existing data-sharing frameworks often fail to adequately address these privacy concerns. To overcome these challenges, this study proposes a blockchain-based, privacy-preserving ecosystem for DNA sequence sharing. The system employs a decentralized architecture to manage digital identities and access permissions, ensuring robust privacy and data security. Smart contract functionalities allow users to assign granular access controls to specific DNA sequence segments, enabling selective sharing with trusted recipients. Furthermore, research institutions are required to obtain certification and classification from governmental authorities, enhancing trust and system reliability. The user-centric design prioritizes privacy, security, and autonomy, simplifying operational processes and fostering user trust. By incentivizing DNA data sharing, the proposed model aims to accelerate medical advancements while maintaining stringent privacy protections, establishing a secure and scalable ecosystem for DNA sequence sharing. Experimental results from a prototype implementation indicate that the system achieves a throughput of up to 10–20 transactions per second for identity and access operations while incurring acceptable on-chain costs (≈1.3 million gas to deploy contracts and 400–800 k gas per user registration). These performance metrics underscore the feasibility and efficiency of the proposed approach.

Applied SciencesVol. 15(6)
Minghsin University of Science and Technology (TW), National Yang Ming Chiao Tung University (TW)
Ministry of Science and Technology, Taiwan
Openalex Percentile: Top 4%
Privacy-Preserving Technologies in Data
7.44
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