Privacy-preserving Cooperative Positioning

Hernández, Guillermo; LaMountain, Gerald; Closas, Pau

doi:10.33012/2020.17531

Cited by 4 publications

(4 citation statements)

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“…APD is a naive approach which can be pursued by exchanging users' location estimates. In this case, pseudonymization or advanced encryption of the shared data must be implemented to ensure privacy [36].…”

Section: Absolute Position Differencementioning

confidence: 99%

“…Of course, the larger is the amount of disclosed GNSS observables, the higher is the capability of the users to accurately guess the location of collaborating agents. Recent advances in homomorphic encription [36] can be promising solutions to overcome such privacy issues in the aforementioned technique but at the cost of additional complexity. To tackle this aspect, the IAR was proposed in [31] as a ranging method that requires a reduced number of shared GNSS observables.…”

Section: Applicabilitymentioning

confidence: 99%

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Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables

Minetto

Nardin

Dovis

2021

Sensors

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In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.

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Section: Absolute Position Differencementioning

confidence: 99%

Section: Applicabilitymentioning

confidence: 99%

Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables

Minetto

Nardin

Dovis

2021

Sensors

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“…The study of distributed data-allocation schemes for CP algorithms is also of increasing interest, as it provides flexibility in data processing [18]. Another important aspect of CP is related to the privacy of collaborative agents, which was explored for instance in [19].…”

Section: Introductionmentioning

confidence: 99%

Massive Differencing of GNSS Pseudorange Measurements

Calatrava

Medina

Closas

2023

2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Global Navigation Satellite Systems (GNSS) is a popular positioning solution able to provide high accuracy, integrity, reliability and high coverage. GNSS performance may be enhanced through aiding systems such as Differential GNSS (DGNSS), which aims to mitigate disruptive sources of error by using corrections sent from a reference station. In this paper, we investigate a method that provides performance results comparable to those by DGNSS without the need for a reference station. We propose the Massive User-Centric Single Difference (MUCSD) algorithm, which leverages a set of collaborative receivers exchanging observables and, potentially, their noisy estimates of position and clock bias. MUCSD is implemented as an iterative weighted least squares (WLS) estimator and its lower accuracy bound, as given by the Cramér-Rao Bound (CRB), is derived as a performance benchmark for the WLS solution. Simulation results are provided as a function of the number of collaborative users and the exchanged information uncertainty. Results show that, without having to access costly-to-maintain reference stations, MUCSD asymptotically outperforms DGNSS as the number of collaborative receivers grows.

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“…In our preliminary work (Hernandez et al, 2020), homomorphic encryption was proposed to leverage privacy in collaborative and differential GNSS (DGNSS) schemes. The current article extends that previous work by broadening the discussion toward different approaches of privacy-preserving methods, including a non-encryption scheme and two encryption-based schemes.…”

mentioning

confidence: 99%