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

TL;DR

This paper tackles privacy challenges in federated time-series analysis by eliciting real-world requirements from medical, Industry 4.0, and smart building domains and mapping them to a spectrum of privacy-preserving technologies. It provides a maturity-aware evaluation of secure computing paradigms—MPC, FHE, FE, TEEs, and FL—and proposes a decision-tree framework to guide application developers in selecting suitable PPTs. The authors identify gaps in efficiency, verifiability, and interoperability, and outline a pragmatic research agenda to advance cross-technique integration and domain-specific solutions. The work aims to empower practitioners to implement privacy-preserving federated time-series analytics while aligning with regulatory expectations and data sovereignty goals.

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.

Figures (1)

• Figure 1: Decision tree supporting the selection of appropriate PPTs for federated time series analysis