ACOUSTIC-TURF: Acoustic-based Privacy-Preserving COVID-19 Contact Tracing

Abstract

In this paper, we propose a new privacy-preserving, automated contact tracing system, ACOUSTIC-TURF, to fight COVID-19 using acoustic signals sent from ubiquitous mobile devices. At a high level, ACOUSTIC-TURF adaptively broadcasts inaudible ultrasonic signals with randomly generated IDs in the vicinity. Simultaneously, the system receives other ultrasonic signals sent from nearby (e.g., 6 feet) users. In such a system, individual user IDs are not disclosed to others and the system can accurately detect "encounters" in physical proximity with 6-foot granularity. We have implemented a prototype of ACOUSTIC-TURF on Android and evaluated its performance in terms of acoustic-signal-based encounter detection accuracy and power consumption at different ranges and under various occlusion scenarios. Experimental results show that ACOUSTIC-TURF can detect multiple contacts within a 6-foot range for mobile phones placed in pockets and outside pockets. Furthermore, our acoustic-signal-based system achieves greater precision than wireless-signal-based approaches when contact tracing is performed through walls. ACOUSTIC-TURF correctly determines that people on opposite sides of a wall are not in contact with one another, whereas the Bluetooth-based approaches detect nonexistent contacts among them.

A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine

“…A contact tracing project called A-Turf was undertaken to accurately detect "encounters" between users within close proximity (e.g. less then six feet) using user feedback reported through a crowdsensing app and acoustic signals emitted by smartphones [108]. By determining the "footprint" of infected individuals, crowdsensing and smartphone sensordriven contact tracing systems help to test, isolate, and treat potential contacts of infected people.…”

“…[102] Monitor whereabouts of infected individuals with WiFi and Bluetooth-based indoor localization and present questionnaire through app to input their memory of historical contacts. [108] Analyze acoustic signals from cellular devices to measure social distance and integrate with user feedback from app to detect infected individuals.…”

WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic

“…Apple and Google [2] are working together for the first time on a protocol that will alert users if they have been exposed to the coronavirus. Luo et al propose A-Turf [20], an acoustic encounter detection method for COVID-19 contact tracing. Compared with Bluetooth technology, the system more precisely detects encounters within 6-foot ranges (social distancing).…”

“…To fight COVID-19, contact tracing [8,12,17,26,27] is especially important to discover active individuals, known as superspreaders, 1 who lead to numerous COVID-19 transmission cases. Tracing human contacts to understand superspreader events is vital for preventing the spread of disease in communities such as university campuses, and such tracing has thus attracted a flurry of research interest [20,30,35,37].…”

“…Client-based approaches require pervasive deployment of apps on people's mobile devices. Client-side apps leverage a wide variety of sources to track "encounters," including records of credit card transactions [24], cryptographic tokens exchanged via Bluetooth Low Energy (BLE) [2,22,29,33], or acoustic channels [20]. In contrast, infrastructure-based methods exploit existing infrastructure deployed worldwide, such as CCTV footage [28], locations measured using cellular networks [3], Wi-Fi hotspots [30], and GPS [4], without requiring client-side involvement.…”