Device Identification in Blockchain-Based Internet of Things

TL;DR

This paper investigates how blockchain transaction data from IoT devices can be de-anonymized using machine learning, and proposes obfuscation techniques to improve user privacy in smart home environments.

Contribution

It introduces a novel analysis of IoT device transaction de-anonymization on blockchain and proposes effective obfuscation methods to protect user privacy.

Findings

Machine learning classifies transactions with 90% accuracy.

Obfuscation methods reduce attack success rates to 20-30%.

Enhanced privacy in IoT blockchain transactions.

Abstract

In recent years blockchain technology has received tremendous attention. Blockchain users are known by a changeable Public Key (PK) that introduces a level of anonymity, however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user de-anonymization focus on monetary applications, however, blockchain has received extensive attention in non-monetary applications like IoT. In this paper we study the impact of de-anonymization on IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify transactions to a particular device that in turn risks the privacy of the users. Two types of attack models are defined: (i) informed attacks: where attackers know the type of devices installed in a smart home, and (ii) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successfully classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20% to 30% and thus enhance user privacy.