VALLE – Privacy Preserving PNT Processing Techniques Concept Demonstrator

This paper presents the development of a concept demonstrator exploring privacy preserving Positioning Navigation and Timing (PNT) concepts using Privacy Enhancing Technologies (PET). The main objectives of this project can be summarised as follows: - To identify, define and consolidate a set of use cases for privacy-preserving positioning solutions or services based on sharing and processing user PNT data. - To define and develop multiple privacy preserving PNT processing concepts based on the sharing and processing of different types of users PNT data. - To design and develop a flexible concept demonstrator for verifying and validating the proposed privacy preserving PNT processing concepts, and for benchmarking (processing time and resource usage, latency, robustness) Several use cases were analysed, focusing on the application of PET techniques to various PNT data types, such as in-phase and quadrature (IQ) samples, observables or position, velocity, and time (PVT) data. The study explored various privacy mechanisms, primarily cryptographic solutions like homomorphic encryption (HE) and secure multiparty computation (SMPC), alongside statistical methods or hardware solutions such as differential privacy (DP) and trusted execution environments, respectively. The analysis shows there is no single PET that solves all privacy challenges across all possible use cases. Therefore, each use case must carefully consider specific objectives to determine the appropriate techniques, balancing privacy complexity and performance. In the case of PNT applications, the use of PET is not yet widespread due to the levels of accuracy or real-time requirements that most of the applications studied require, with Location Based Services (LBS) applications being the ones that most frequently introduce PET techniques for processing PNT data. To demonstrate the concept of PNT applications using PET, three use cases were implemented in a flexible concept demonstrator, each targeting specific PNT data types and PET. The demonstrator employs advanced methods such as partially homomorphic encryption (PHE) to securely process data without revealing it, anonymization and secure multiparty computation (SMPC) to enable private computations across multiple parties. This paper presents the analysis, design and implementation of a flexible concept demonstrator, providing insight into PET solutions for private cloud computation in several applications where PNT plays major roles.