论文标题
Monero网络中的机器学习可追溯性和交易值的模拟区块链
Simulated Blockchains for Machine Learning Traceability and Transaction Values in the Monero Network
论文作者
论文摘要
Monero是一种流行的加密货币，侧重于隐私。区块链使用加密技术来模糊交易值以及“环机密交易”，该交易旨在隐藏可变数量的欺骗交易之间的真实交易。我们已经开发了10个和50个代理的模拟区块链的培训集，为此我们可以控制地面真理和钥匙，以测试这些主张。我们通过表征与公众面向公共区块链的局部结构并使用从模拟获得机器学习的标签来表征Monero交易的特征。在模拟区块链上对我们功能的机器学习表明，该技术可用于辅助识别个人和群体，尽管它没有成功揭示隐藏的交易值。我们将技术应用于真正的Monero区块链上，以识别Shapeshift交易，这是一种通过其API泄露信息的加密货币交易所，为自己和用户提供标签。
Monero is a popular crypto-currency which focuses on privacy. The blockchain uses cryptographic techniques to obscure transaction values as well as a `ring confidential transaction' which seeks to hide a real transaction among a variable number of spoofed transactions. We have developed training sets of simulated blockchains of 10 and 50 agents, for which we have control over the ground truth and keys, in order to test these claims. We featurize Monero transactions by characterizing the local structure of the public-facing blockchains and use labels obtained from the simulations to perform machine learning. Machine Learning of our features on the simulated blockchain shows that the technique can be used to aide in identifying individuals and groups, although it did not successfully reveal the hidden transaction values. We apply the technique on the real Monero blockchain to identify ShapeShift transactions, a cryptocurrency exchange that has leaked information through their API providing labels for themselves and their users.
