Practical Privacy-Preserving Indoor Localization based on Secure Two-Party Computation

We present a privacy-preserving indoor localization scheme based on received signal strength measurements, e.g., from WiFi access points. Our scheme preserves the privacy of both the client's location and the service provider's database by using secure two-party computation instantiated with known cryptographic primitives, namely, Paillier encryption and garbled circuits. We describe a number of optimizations that reduce the computation and communication overheads of the scheme and provide theoretical evaluations of these overheads. We also demonstrate the feasibility of the scheme by developing a proof-of-concept implementation for Android smartphones and commodity servers. This implementation allows us to validate the practical performance of our scheme and to show that it is feasible for practical use in certain types of indoor localization applications.Peer reviewe

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Privacy preserving in indoor fingerprint localization and radio map expansion

People spend most of their life time in indoor environments and in all of these environments, Location Service Providers (LSPs) improve users’ navigation. Preserving privacy in Location Based Services (LBSs) is vital for indoor LBSs and fingerprinting based indoor localization method is an emerging technique in indoor localization. In such systems, LSP may be curious and untrusted. Therefore, it is preferred that user estimates its location by using a Partial Radio Map (PRM) which is achieved by LSP, anonymously. In this paper, a privacy preserving method that uses Bloom filter for preserving anonymity and creating PRM during localization process, is proposed. In this method, LSP cannot recognize user identity, which is anonymized by the anonymizer. The proposed method has lower computational complexity compared with methods that use encryption or clustering concepts. The proposed method also has higher accuracy in localization compared with those that use Bloom filter with one random selected AP. Then, in order to decrease the complexity and to increase the accuracy at the same time, we introduce a method that expands the radio map by authenticated users, without compromising their privacy. We also enhance the performance of this method, using Hilbert curve for preserving the ambiguity of users’ location. After verifying the user’s data, LSP sends a certificate to the authenticated users. This certificate can increase the priority of users in LBS requests. Simulation results and measurements show that the proposed method on average improves the localization accuracy up to 16% compared with existing location privacy methods

FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices

Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x better localization accuracy on average than the best performing FL-based indoor localization framework from prior work

COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing

Abstract The way people access services in indoor environments has dramatically changed in the last year. The countermeasures to the COVID-19 pandemic imposed a disruptive requirement, namely preserving social distance among people in indoor environments. We explore in this work the possibility of adopting the indoor localization technologies to measure the distance among users in indoor environments. We discuss how information about people's contacts collected can be exploited during three stages: before, during, and after people access a service. We present a reference architecture for an Indoor Localization System (ILS), and we illustrate three representative use-cases. We derive some architectural requirements, and we discuss some issues that concretely cope with the real installation of an ILS in real-world settings. In particular, we explore the privacy and trust reputation of an ILS, the discovery phase, and the deployment of the ILS in real-world settings. We finally present an evaluation framework for assessing the performance of the architecture proposed

PILOT : Practical Privacy-Preserving Indoor Localization Using OuTsourcing

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.Peer reviewe

Privacy in Indoor Positioning Systems: A Systematic Review

Ponència presentada a 10th International Conference on Localization and GNSS (ICL-GNSS), celebrada a Tampere (Finland) del 2 al 4 de juny de 2020This article presents a systematic review of privacy in indoor positioning systems. The selected 41 articles on location privacy preserving mechanisms employ non-inherently private methods such as encryption, k-anonymity, and differential privacy. The 15 identified mechanisms are categorized and summarized by where they are processed: on device, during transmission, or at a server. Trade-offs such as calculation speed, granularity, or complexity in set-up are identified for each mechanism. In 40% of the papers, some trade-offs are minimized by combining several methods into a hybrid solution. The combinations of mechanisms and their levels of offered privacy are suggested based on a series of user mobility cases

Real-time localisation system for GPS denied open areas using smart street furniture

Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems. Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities. In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices. Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved