The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure. However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats. Adversaries carry out privacy-oriented attacks to gain access to the sensitive and confidential data of critical infrastructure for various self-centered, political and commercial gains. In the past, researchers have employed several privacy preservation approaches including cryptographic encryption and k-anonymity to secure IoT-enabled critical infrastructure. However, for various reasons, those proposed solutions are not well suited for modern IoT-enabled critical infrastructure. Therefore, Dwork's differential privacy has emerged as the most viable privacy preservation strategy for IoT-enabled critical infrastructure. This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids (SGs), Intelligent Transport Systems (ITSs), healthcare and medical systems, and Industrial Internet of Things (IIoT). Finally, we discuss some promising future research directions in differential privacy for IoT-enabled critical infrastructure.

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As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are individual market participants and liable to ensure the privacy of their own customers. To address this issue, we propose a novel horizontal privacy-preserving federated learning framework for REPs energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and scalability. For forecasting, we use a state-of-theart Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations and promises of higher accuracy with time-series data while solving the vanishing gradient problem. Finally, we conduct extensive data-driven experiments using a real energy consumption dataset. Experimental results demonstrate that our proposed federated learning framework can achieve sufficient performance in terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a centralized approach while preserving privacy and improving scalability.

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