【Author】
Song, Yuhan; Wei, Fushan; Zhu, Kaijie; Zhu, Yuefei
【Source】IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
【Abstract】Attacks against blockchain networks have proliferated in recent years. Due to its immense economic value, Bitcoin has been subject to numerous malicious theft activities through the exchange platforms. This poses a severe threat to the credibility of the entire Bitcoin ecosystem. Therefore, it is necessary to provide detection and prediction services of malicious events for Bitcoin Exchanges to prevent them in a precise and timely manner. Meanwhile, preserving the privacy of transaction data to prevent de-anonymization attacks during the detection process is also of great importance. In this paper, we present a general framework for privacy-preserving anomaly detection in blockchain networks. Based on this framework, we propose ADaaS, an anomaly detection service scheme that adopts a supervised machine learning model and achieves privacy preservation by using vector homomorphic encryption and matrix perturbation strategies. We also analyze the security, communication and computation costs of ADaaS. Experimental results demonstrate that ADaaS can achieve high detection effectiveness while providing privacy guarantees and is applicable in real scenarios of detecting Bitcoin transactions due to its reasonable efficiency.
【Keywords】Bitcoin; Anomaly detection; Blockchains; Privacy; Computational modeling; Perturbation methods; Homomorphic encryption; Blockchain; anomaly detection; security and privacy; vector homomorphic encryption; machine learning
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