FESCAT: Function Secret Sharing Based Efficient Secure Collaborative Analysis of Time Series Data

Time series data analysis, employing dynamic time warping (DTW) algorithms, has a wide range of applications in fields such as medicine and economics. Given the widespread distribution of data across different domains, integrating and analyzing these datasets through outsourced cloud computing can enhance analytics, though privacy concerns arise. Privacy preserving data analysis, underpinned by secure multi-party computing, emerges as a crucial approach to address this challenge. However, existing efforts face high communication costs and increased interactions, resulting in significant efficiency constraints in practical applications. In this paper, we propose a function secret sharing (FSS)-based framework for secure collaborative analysis of time series data using the DTW algorithm. Utilizing the distributed comparison function, we develop efficient building blocks with minimal online interaction and communication, enhancing the practicability of security protocols. To address the challenges of FSS key generation due to uncertain computational topology when cascading multiple distances, we adopt a modular design and decompose the analysis process into several critical modules. Furthermore, our framework efficiently supports various constraint methods for DTW. We implement and evaluate our framework using publicly available datasets. The results demonstrate a significant reduction in communication costs and the number of interactions during the online phase.