WCSE 2020 Summer
ISBN: 978-981-14-4787-7 DOI: 10.18178/wcse.2020.06.065

Clustering-Based Approach for Private Chain Data Anonymization

Quan Jiang, Bin Qu, Siyu Li, Li-e Wang, Xianxian Li

Abstract— Blockchain is a new type of distributed data storage technology. With the rapid development of blockchain, vast amounts of data have been accumulated in these applications, which provide researchers with unprecedented opportunities to analyze blockchain data. However, if blockchain data is published openly, it may cause privacy leaks. Owing to the characteristics of blockchain data, the traditional anonymous method based on data publishing cannot be directly applied to blockchain data. This paper proposes an anonymous method based on clustering named clustering partition based on Bisecting k-medoids (CP-BK). We treat the transaction data of the blockchain as table data and use the k-anonymity model to protect the identity privacy of blockchain users. Finally, we evaluated the information loss and efficiency of this algorithm in experiments

Index Terms— Blockchain, Privacy Protection, Data Publish, K-anonymous, Clustering

Quan Jiang, Bin Qu, Siyu Li, Li-e Wang, Xianxian Li
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, CHINA
Guangxi Key Lab of Multi-source Information Mining & Security, College of computer science & information engineering, Guangxi Normal University, CHINA


Cite: Quan Jiang, Bin Qu, Siyu Li, Li-e Wang, Xianxian Li "Clustering-Based Approach for Private Chain Data Anonymization" Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp.440-449 , Shanghai, China, 19-21 June, 2020.