A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.Peer reviewe

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

Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA

Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in a real-world testbed demonstrates PassiFi's exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy

POSTER: Privacy-preserving Indoor Localization

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec'14

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