The European Space Agency-funded VALLE project, led by GMV and with contributions by RISE, is developing innovative, privacy-preserving solutions for positioning, navigation, and timing (PNT) applications.
Focusing on anonymization, homomorphic encryption and secure multiparty computation (SMPC), the project addresses key use cases such as collaborative positioning, crowd management, and secure GNSS signal correlation.
As PNT technologies become critical for both civilian and military applications, ensuring user privacy without sacrificing accuracy is essential. Adversaries, including criminals, hackers, state actors and/or agents of industrial espionage, can exploit unprotected GNSS signals to disrupt or manipulate PNT information, leading to potential breaches of privacy and security.
VALLE uses SMPC to perform privacy-preserving density calculations that enhance crowd management while protecting individual identities, while homomorphic encryption enables GNSS signal correlation in encrypted domains, offering a secure alternative to conventional processing methods. Anonymization techniques can also be employed to facilitate collaborative positioning, yielding high-accuracy PNT outcomes while safeguarding user data.
The project’s demonstrator and performance benchmarks have confirmed the computational feasibility of these techniques, with analyses verifying robustness against privacy vulnerabilities. These promising results point to potential applications in a number of non-space applications.
Satisfying conclusion
At the recent VALLE final project presentation hosted by ESA, GMV Big Data Engineer Jedrzej Mosieznyn highlighted methods that balance data privacy with strong PNT performance. The VALLE team consolidated various use cases for privacy-preserving positioning services based on collected user PNT data, developing multiple processing concepts validated by a flexible demonstrator.
Key achievements included the use of SMPC for fast, secure computation of user density on personal computers, enabling efficient data sharing for location-based services. Partially homomorphic encryption allowed IQ sample correlation within encrypted domains on a single server-class system, opening opportunities for further algorithm enhancements. In addition, anonymization of GNSS observables supported a collaborative positioning solution that delivered high-accuracy results.
Network traffic analysis during the project revealed consistent patterns in ICMPv6, mDNS, and ARP protocols, with no vulnerabilities detected. Looking ahead, the VALLE solution shows real potential for use in a broad variety of applications such as crowd management, contact tracking, IoT, and smart city services. GMV intends to further refine these privacy-preserving PNT concepts and advance their technology readiness level (TRL) for next-generation, secure PNT systems.
The VALLE project ‘Novel Privacy-Preserving PNT Processing Techniques’, was funded under NAVISP Element 1, which supports innovation and disruptive technologies across the European PNT value chain.
