Abstract
Constructing covert channels on blockchains has recently become a significant research focus. A major challenge lies in embedding data into blockchain transactions, while maintaining strong concealment. This study focuses on utilizing blockchain transaction values as a carrier, exploring methods to ensure that the distribution of values with embedded data mimics that of standard transactions. Initially, we gathered authentic blockchain transaction data, conducted a frequency analysis on the transaction values, and filtered out abnormal values to establish a baseline normal value dataset. Subsequently, we employed a Restricted Boltzmann Machine (RBM) to generate a simulated value dataset that mirrors the distribution of the normal dataset, facilitating the embedding of data within the simulated value dataset. By fine-tuning the number of bits embedded in the values, we achieved a value dataset with embedded data that closely resembles the normal distribution. We have calculated the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test between the value dataset with embedded data and the normal value dataset under various embedding scenarios, revealing that the changes to the original dataset are minimal.
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Acknowledgement
This work is supported by the the National Key R&D Program of China under Grant No. 2022YFB2701500 and Guangdong Provincial Key Laboratory of Information Security Technology (No.2023B1212060026).
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An, J., Tian, H. (2025). Embedding Data in Bitcoin Transaction Values with Restricted Boltzmann Machine. In: Chen, X., Huang, X., Yung, M. (eds) Data Security and Privacy Protection. DSPP 2024. Lecture Notes in Computer Science, vol 15215. Springer, Singapore. https://doi.org/10.1007/978-981-97-8540-7_2
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DOI: https://doi.org/10.1007/978-981-97-8540-7_2
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