Record linkage is the challenging task of deciding which records, coming from disparate data sources, refer to the same entity. Established back in 1946 by Halbert L. Dunn [1], the area of record linkage has received tremendous attention over the years due to its numerous real-world applications, and has led to a plethora of technologies, methods, metrics, and systems. A major direction in record linkage regards methods for linking records in a privacy-preserving manner, where sensitive and personally identifiable information in the records is not leaked as part of the linkage process. In this article, we provide an overview of the large body of research literature in privacy-preserving record linkage, discuss the different generations of techniques that have been proposed, their advantages and limitations, and present a taxonomy as well as an extensive survey on the latest generation of methods. We conclude this work with a roadmap to the new generation of analytics-driven techniques that aims to address some of the major challenges in the field.

Cyber-Physical Systems (CPS) underpin global critical infrastructure, including power, water, gas systems and smart grids. CPS, as a technology platform, is unique as a target for Advanced Persistent Threats (APTs), given the potentially high impact of a successful breach. Additionally, CPSs are targets as they produce significant amounts of heterogeneous data from the multitude of devices and networks included in their architecture. It is, therefore, essential to develop efficient privacy-preserving techniques for safeguarding system data from cyber attacks. This paper introduces a comprehensive review of the current privacy-preserving techniques for protecting CPS systems and their data from cyber attacks. Concepts of Privacy preservation and CPSs are discussed, demonstrating CPSs' components and the way these systems could be exploited by either cyber and physical hacking scenarios. Then, classification of privacy preservation according to the way they would be protected, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are explained to illustrate how they would be employed for data privacy preservation. Finally, we show existing challenges, solutions and future research directions of privacy preservation in CPSs.