Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers

TL;DR

This paper reviews privacy-preserving technologies for federated time series analysis, providing a decision framework for application developers and identifying research gaps to enhance privacy and efficiency.

Contribution

It offers a comprehensive qualitative requirements analysis, technology matching, maturity assessment, and a decision tree for selecting suitable privacy-preserving methods in federated time series analysis.

Findings

Identified key privacy requirements for federated time series analysis.

Mapped available technologies to these requirements and assessed their maturity.

Highlighted gaps and future research directions in privacy-preserving technologies.

Abstract

The federated analysis of sensitive time series has huge potential in various domains, such as healthcare or manufacturing. Yet, to fully unlock this potential, requirements imposed by various stakeholders must be fulfilled, regarding, e.g., efficiency or trust assumptions. While many of these requirements can be addressed by deploying advanced secure computation paradigms such as fully homomorphic encryption, certain aspects require an integration with additional privacy-preserving technologies. In this work, we perform a qualitative requirements elicitation based on selected real-world use cases. We match the derived requirements categories against the features and guarantees provided by available technologies. For each technology, we additionally perform a maturity assessment, including the state of standardization and availability on the market. Furthermore, we provide a decision tree supporting application developers in identifying the most promising technologies available matching their needs. Finally, existing gaps are identified, highlighting research potential to advance the field.