On the Activity Privacy of Blockchain for IoT
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In recent years blockchain has received tremendous attention as a means to provide a distributed, secure, auditable, and anonymous framework for the Internet of Things (IoT). In an IoT-based blockchain, the communications between IoT devices are stored in the blockchain in the form of transactions. Similar to other existing blockchains, IoT users and devices use changeable Public Keys (PKs) as their identity in an attempt to remain anonymous. However, recent studies in crytocurrency demonstrated that the users can be deanonymized by analyzing the pattern of transactions. In this paper, we study the success rate of classifiying IoT devices using the stored transactions in the blockchain which potentially may compromise user anonymity. To the best of our knowledge, this paper is the first attempt to analyze device classification in blockchain-based IoT. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90 namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20
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