Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

This article proposes a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on estimated user mobility cases.