Privacy-Protecting COVID-19 Exposure Notification Via Cluster

Events Without Proximity Detection

Paul Syverson

U.S. Naval Research Laboratory

paul.syverson@nrl.navy.mil

We provide a rough sketch of a simple system design for exposure notification of COVID-19 infections

based on copresence at cluster events—locations and

times where a threshold number of tested-positive

(TP) individuals were present. Unlike other designs,

such as DP3T or the Apple-Google system, this design does not track or notify based on detecting direct

proximity to TP individuals.

The design makes uses existing or in-development

tests for COVID-19 that are relatively cheap and return results in less than an hour, and that have high

specificity but may have lower sensitivity. It also

makes use of readily available location tracking for

mobile phones and similar devices. It reports events

at which TP individuals were present but does not

link events with individuals or with other events in

an individual’s history. Participating individuals are

notified of all detected cluster events. They can compare these locally to their own location history. Detected cluster events can be publicized through public

channels. Thus, individuals not participating in the

reporting system can still be notified of exposure.

Unlike common influenzas and other familiar diseases, COVID-19 appears to have a high degree of

clustering in its dispersion and to be spread more

in events where infected individuals spend time in

close proximity to groups of others. There are additional dispersion clustering factors, such as whether

gatherings are indoors and adequacy of ventilation.

Clustering also occurs around some people who are

individually linked to high number of infections. But

generally, gatherings play a significant role in infection rates, whether or not they are exacerbated by

other factors [5]. This includes both super-spreader

large gatherings for social or cultural events as well

as more moderate sized gatherings [1].

Most tests for COVID-19 initially rolled out required specialized equipment to evaluate collected

samples, required days or more to return results, were

expensive, or all of the above. But analyses indicate

that faster, cheaper point-of-care tests can be more

effective at identification of infected individuals than

more sensitive tests that are slower [6, 4]. And HHS

in partnership with the the DoD is now providing

rapid point-of-care tests to communities across the

U.S. [3]. Similarly, under the auspices of the WHO,

a global partnership has plans to soon make 120 million rapid tests available in low- and middle-income

countries [8]. Analyses of effectiveness are focused on

identification and notification of infectious individuals. But coupled with dispersion patterns, another

advantage of cheap, point-of-care tests emerges.

“[G]iven the huge numbers associated with these

clusters, targeting them would be very effective in

getting our transmission numbers down” [7]. Also,

identifying a cluster does not require that everyone

who was already infected at a particular event (location and time) has tested positive or even has been

tested at all. As long as a sufficient number of TP

individuals are associated with a given event, it is not

important to identify which individuals were present,

even pseudonymously, in order to indicate that the

event constitutes a cluster. Since cluster events are

indicators of significant risk of infection for all copresent at the event, informing individuals of those

clustering events at which they were present is sufficient to notify them of potential exposure. And,

individuals can make the determination of whether

they were present at a clustering event entirely locally by comparing notification of clustering events

with the location history in their phones.

Another potential advantage of a cluster-event

based approach is that, even without limiting TP

reporting to authorized individuals, it provides automatic counters to the possibility that “people may

falsely report they have been infected to cause mischief or to keep people home in order to shut down

school or even to disrupt an election” [2]. Someone

falsely reporting a positive test cannot easily create

such a result because they are unlikely to be the one

to transition a location and time across the threshold of counting as a cluster event. And they cannot

report at all for a location and time unless they were

present at that event. Our cluster-event design thus

automatically prevents, e.g., a student who is worried

about an upcoming exam and anonymously reports a

positive test from thereby causing his school to shut

down or his whole chemistry class to be forced into

quarantine. A fan of one sports team cannot trivially

force a rival team into quarantine, etc.

1
References

[1] Muge Cevik, Julia Marcus, Caroline Buckee, and

Tara Smith. SARS-CoV-2 transmission dynamics should inform policy. https://ssrn.com/

abstract=3692807, September 14 02020.

[2] Lorrie Faith Cranor. Digital contact tracing may

protect privacy, but it is unlikely to stop the pandemic. Communications of the ACM, 63(11):22–

24, November 02020.

[3] COVID-19 rapid point-of-care test distribution.

https://www.hhs.gov/coronavirus/testing/

rapid-test-distribution/index.html, October 7 02020.

[4] Lee Kennedy-Shaffer, Michael Baym, and

William Hanage. Perfect as the enemy of

the good: Using low-sensitivity tests to mitigate SARS-CoV-2 outbreaks. https://dash.

harvard.edu/handle/1/37363184, 02020.

[5] Sadamori Kojaku, Laurent H ́ebert-Dufresne,

Enys Mones, Sune Lehmann, and Yong-Yeol

Ahn. The effectiveness of backward contact tracing in networks. https://arxiv.org/abs/2005.

02362, May 5 02020.

[6] Daniel B. Larremore, Bryan Wilder, Evan

Lester, Soraya Shehata, James M. Burke,

James A. Hay, Milind Tambe, Michael J.

Mina, and Roy Parker. Test sensitivity is

secondary to frequency and turnaround time

for COVID-19 surveillance. medRxiv, 02020.

https://www.medrxiv.org/content/early/

2020/09/08/2020.06.22.20136309.full.pdf.

[7] Zeynep Tufekci. This overlooked variable is

the key to the pandemic: It’s not R. The

Atlantic, September 30 02020. https://www.

theatlantic.com/health/archive/2020/09/

k-overlooked-variable-driving-pandemic/

616548/.

[8] Global partnership to make available 120

million affordable, quality COVID-19 rapid

tests for low- and middle-income countries.

https://www.who.int/news/item/28-09-2020-

global-partnership-to-make-available-120-

million-affordable-quality-covid-19-rapidtests-for-low--and-middle-income-countries,

September 28 02020.

2
Augmenting GAEN with opt-in case linking

Google and Apple’s Exposure Notification system (GAEN) can be augmented to 

allow users to provide additional information to their health authority. This 

information would allow contact tracers to integrate contacts notified via GAEN 

into their existing case management systems. Doing so would enable more 

accurate risk assessment, reduce duplicated work, and enable notified individuals 

to hear tailored advice. The primary modifications needed are allowing health 

authorities to store a set of Temporary Exposure Keys (TEKs) with a case file, and 

allowing notified individuals to send TEKs that that they matched with to their 

public health authority.

The modified system would work the same as the current GAEN implementation if 

neither the positive individual or contact opts in to share additional information. If 

the positive individual opts in, then their TEKs are stored with the rest of their case 

data in the case management system. If the contact opts in, then their name and 

phone and exposure notification data (including the TEKs matched with) are sent 

securely to the contact’s public health authority. The time and location of 

exposure could also be sent to the health authority and access could be restricted 

unless both parties have opted in. In scenarios where both people have opted in, 

contact tracers would be able to see who was involved in a potential exposure, the 

(estimated) proximity and duration of exposure, and optionally the time and 

location. They would be able to compare this information with notes collected 

from the case investigation and provide more tailored risk assessments. If capacity 

is low, they could de-prioritize calling people have already received a notification. 

Cluster analysis and backward contact tracing would also be be enabled.

Currently GAEN works almost independently from manual contact tracing. 

Combining information from both systems could improve risk assessment, 

quarantine compliance, and cluster analysis, without significant loss of privacy or 

increased workload.
Adoption Metrics for Proximity Technologies

By: Scott L. David, Director of Information Risk Research Initiative (IRRI), 

University of Washington Applied Physics Laboratory

Seattle, Washington

The Need:

We need metrics to help promote and steer adoption of Exponential Socio-Technical Systems (ESTS)

ESTS are systems that are: 

(1) powered by higher-order effects of Moore’s law, 

(2) which rely on “network effects” for value, and 

(3) depend on reliable/predictable performance/behaviors of humans and institutions as BOTH users 

AND as critical system components and data providers.

Our Approach:

IRRI creates programs and materials that support distributed testing ecosystems AND which smooth the 

pathway to adoption/commercialization of “technically feasible” technologies.

Source of the Problems is the Source of the Solutions:

Where the success of the technology is dependent on sufficient adoption and deployment across a large 

segment of a given population, it is critical that testing go beyond mere “technical feasibility” and 

consider additional factors affecting stakeholder uptake.

IRRI programs test “technically feasible” technologies for adoptability through “BOLTS” reasonableness.

BOLTS tests are based on metrics drawn from Business, Operations, Legal, Technical and Social domains.

IRRI supplies the process and artifacts for thorough and complete mapping of the threat, vulnerability, 

risk, and opportunity landscape into which any new technologies are introduced, including those 

associated with proximity detection in pandemics and other public health settings.

Benefits of IRRI approach:

BOLTS tests are familiar to stakeholders because they reflect patterns of practice, language, and 

interaction rules drawn from real world settings. IRRI processes enable stakeholder discovery of new 

solutions to interaction risks (e.g., privacy, security, liability) that none of them can achieve unilaterally.

Other pathways to testing ecosystems and commercialization:

Many other current forms of technology transfer facilitation and institutionalization are not sufficiently 

broad and/or do not sufficiently address multiple stakeholders’ needs to fully gauge potential for 

adoption of new technologies. IRRI’s distributed testing approach can help to bring together existing 

pathways to enhance technology testing and commercialization, particularly in the case of ESTS.
Modeling the impact of automatic exposure notification for vulnerable communities

Krister Shalm

Applied Physics Division, NIST and Physics Department, University of Colorado, Boulder, CO

While most efforts in automatic exposure notification focus on large population groups, we study the 

effectiveness of such systems in smaller vulnerable communities. For example, it has been estimated 

that over one-third of COVID-19 related deaths have occurred among nursing home residents in the US. 

Understanding what criteria must be met to successfully deploy interventions such as automatic 

exposure notification in smaller, targeted, communities could lead to a major health impacts in this and 

future pandemics. Smaller communities also tend to be more homogeneous, and it might be more 

feasible to achieve high adoption and compliance rates with such groups. In this talk we will report some 

of the recent results from our modeling efforts that compare the effectiveness of automatic exposure 

notification to simpler quarantine and testing strategies and show how the performance of devices used 

for tracking encounters in small communities is important.
Understanding and Rewiring Epidemic Networks: A Data-driven 

Approach Towards Enabling Quarantine in-Motion 

Radu Marculescu 

Department of Electrical & Computer Engineering 

The University of Texas at Austin 

radum@utexas.edu

In 2020, the world grinded to a halt to fight against an invisible enemy: COVID-19. The virus 

traveled swiftly through today’s highly connected world. Though we find ourselves still actively 

trying to mitigate the current pandemic, one wonders how can we learn from our mistakes and 

grow resilient to other infectious diseases in the process? 

Imagine yourself being able to visualize the risk of exposure to a virus while on your commute to 

work or while running errands around the town. Imagine that you and your friends are able to 

know first-hand when any possible infectious disease is spreading throughout your local 

community, even before local authorities begin raising alarms. Imagine knowing exactly when

and where to avoid going to spend your evenings or weekends. If a mobile application gave you 

a safer route to lower your exposure to an infectious disease, would you take it? 

This kind of application is precisely what the aim of this project is. More precisely, based on GPS 

mobility data, we try to identify the safe paths during an epidemic and minimize an individual’s 

exposure to a virus. Similar to a navigation app that detours users away from traffic accidents, 

this application can direct users to avoid exposure to a virus. 

To this end, this project targets human mobility at micro-, meso-, and macro-scale via a datadriven and machine learning-based approach. In other words, we are concerned with people’s 

mobility in relatively small areas such as buildings or shared pedestrian ways, such that the 

trajectory and speed of a person changes due to crowd dynamics, traffic congestion, social 

interactions, or various other reasons. At mesoscale, we are concerned with people’s mobility 

across larger areas, say an entire neighborhood or section of a city. Finally at macro-scale, we are 

looking at social sensing and phenomena induced by mobility over large areas, e.g. across a city 

or even an entire county. 

As we are continue our research in this project, we hope to discover how modern mobility can 

grow resilient to infectious disease via trajectories and contact networks rewiring. When fully 

deployed, this can be a gamer changer as it would enable what we call “Quarantine in Motion”, 

meaning a way of letting people go by their business while navigating the neighborhoods (and 

the city) in the safest possible way. 
Interoperable Privacy Preserving Digital Contact Tracing

Yaling Yang (Virginia Tech, yyang8@vt.edu)

During a pandemic, contact tracing is an essential tool to drive

down the infection rate within a population. To accelerate the laborious manual contact tracing process, smartphone-based digital

contact tracing (DCT) tools have been proposed. These systems estimate proximity either through geolocation (e.g. Global Navigation

Satellite System (GNSS)), or wireless technologies like Bluetooth.

Since the onset of COVID-19, various DCT systems have been

developed and deployed around the world. A majority of them identify contacts using Bluetooth rather than using GNSS due to privacy

concerns. However, if the privacy issue can be solved, geolocationbased DCT systems will offer two major advantages over Bluetoothbased systems.

First, geolocation-based DCT systems are compatible with manual contact tracing, whereas Bluetooth-based systems are not. As

of today, most of the contact tracing work is carried out manually.

Contact tracers work with patients to identify their close contacts

and the places they visited a few days before the onset of symptoms

[7]. Apart from obtaining identities of close contacts from patients,

manual contact tracing also identifies locations patients visited.

This verifies contacts whom the patient does not personally know,

such as a cashier in a supermarket or a server in a restaurant [2, 4].

Unfortunately, Bluetooth-based and other proximity-sensing-based

DCT systems do not collect such absolute location data. Hence,

they can neither benefit from nor aid manual contact tracing.

In comparison, geolocation-based DCT systems and manual contact tracing can exchange location information for mutual benefits. For example, a geolocation-based DCT can provide exposure

risk levels in public areas (e.g. restaurants and grocery stores) to

help manual contact tracing to identify high-risk service workers.

A contact tracer can share a patient’s past visited locations to a

geolocation-based DCT system even if the patient did not use the

DCT app before the onset of symptoms. By getting additional patient histories from manual contact tracing, geolocation-based DCT

systems can warn more users and may achieve a reduction in disease

spreading at a lower app adoption rate. This makes geolocationbased DCT systems useful under a gradual deployment timeline,

and in a demographic where a large portion of the population does

not have the app. The latter scenario reflects reality. In the US for

example, only 53% of US seniors older than 65 own smartphones

[14]. Yet, this age bracket is the most vulnerable to the disease.

Second, geolocation-based DCT systems can inform the health

authorities about the spread of the disease in a particular location.

By aggregation of patients’ trajectory history, the health authorities

may learn the disease’s spreading pattern within a community, and

make informed decisions such as allocating more medical resources

to hot-spot areas , announcing lockdown for a specific region instead of the whole state, or warn about a recent high-risk activity

to a local community. Unfortunately, Bluetooth-based DCT systems

cannot provide such information to the health authorities.

Challenges: Despite the above obvious benefits of geolocationbased DCT systems, they face two major design challenges:

(1) How can a DCT system preserve all users’ privacy while

recording their past trajectories?

(2) How to minimize the effects of GNSS localization error in

contact tracing, especially in complex environments like

indoor or urban canyon environments?

The first challenge is critical since privacy concerns have greatly

hindered the wide adoption of DCT apps [8, 13]. We consider two

perspectives: (a) trajectory privacy that protects a user’s visited

places and any personal information inferred from the trajectory

histories; (b) exposure privacy that hides the user’s exposure risk

provided by the DCT app. To protect users’ trajectory privacy, we

propose to use k-anonymity to mix users’ real trajectories with

synthesized trajectories, such that users’ real trajectories are hidden

from eavesdroppers and other parties in the DCT system [3, 10, 12].

Next, we protect users’ exposure risk through an efficient multiparty computation (MPC) design. MPC is a secure computation

technique, where a function is computed by multiple parties jointly

over their input, while keeping these inputs and the output private.

However, designing an MPC-based DCT protocol is nontrivial since

MPC for general functions is currently not scalable due to its huge

computational complexity [5]. To enable our MPC scheme to compute trajectory intersection efficiently even when a large number of

synthesized trajectories exist, we customized the MPC scheme with

a partial-homomorphic Paillier cryptosystem [9], where the computation of location intersection and exposure risk is decomposed

into a small set of homomorphic operations.

For the second challenge, we propose to integrate the Bluetooth

technology with our geolocation-based DCT system, as Bluetooth

has a better accuracy of proximity detection than GNSS in indoor environments. However, existing Bluetooth-based methods

[1, 6, 11, 15] cannot fit into our geolocation-based design. A naive

integration of Bluetooth and geolocation-based DCT system may

cause a privacy risk which may be exploited by curious users to

deanonymize patients’ identities using exposure locations. To prevent this problem, we designed our Bluetooth protocol to be compatible with our geolocation-based DCT system, which maintains

k-anonymity based user privacy.

Overall, in this paper, we propose KHOVID: a Hybrid DCT system that enhances geolocation-based contact tracing with Bluetooth

while preserving user privacy using K-anonymity. In KHOVID,

degradation of user privacy is avoided by building a customized

MPC mechanism using the Paillier cryptosystem. In addition, to increase the accuracy of contact detection in complex environments,

we combine the geolocation-based method with Bluetooth technology by designing a new Bluetooth DCT protocol. KHOVID offers

accuracy and availability for multiple physical environments, interoperability with manual contact tracing, and the preservation of

the privacy of all users in the system.
REFERENCES

[1] Apple and Google. 2020. Exposure Notifications: Using technology to help

public health authorities fight COVID-19. Retrieved Dec 11, 2020 from https:

//www.google.com/covid19/exposurenotifications/

[2] BBC. 2020. Coronavirus: How Does Contact Tracing Work? BBC News (2020).

https://www.bbc.com/news/explainers-52442754

[3] V. Bindschaedler and R. Shokri. 2016. Synthesizing Plausible Privacy-preserving

Location Traces. In Proceedings of 2016 IEEE Symposium on Security and Privacy

(SP). IEEE.

[4] USA CDC. 2020. Key Information to Collect During a Case Interview.

(2020). https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/

keyinfo.html

[5] Y. Dou, K. Zeng, H. Li, Y. Yang, B. Gao, K. Ren, and S. Li. 2016. p

2

-SAS: Privacypreserving Centralized Dynamic Spectrum Access System. IEEE Journal on

Selected Areas in Communications (2016).

[6] Inria and Fraunhofer AISEC. 2020. ROBERT-proximity-tracing/documents.

Retrieved Dec 11, 2020 from https://github.com/ROBERT-proximity-tracing/

documents

[7] L. Jaffe. 2020. What’s It Like to Be a Contact Tracer? Probulica Illinois

(2020). https://www.propublica.org/article/whats-it-like-to-be-a-contact-tracerwe-spoke-with-3-to-find-out

[8] C. Lehmann. 2020. Privacy Concerns Hindering Digital Contact Tracing. WebMD (2020). https://www.webmd.com/lung/news/20200928/privacyconcerns-hindering-digital-contact-tracing

[9] P. Paillier. 1999. Public-key Cryptosystems Based on Composite Degree Residuosity Classes. In Proceeding of International conference on the theory and applications

of cryptographic techniques. Springer, 223–238.

[10] L. Pappalardo and F. Simini. 2018. Data-driven Generation of Spatio-temporal

Routines in Human Mobility. Data Mining and Knowledge Discovery (2018).

[11] ppep pt.org. 2020. PEPP-PT Pan-European Privacy-Preserving Proximity Tracking. Retrieved Dec 11, 2020 from https://github.com/pepp-pt/pepp-ptdocumentation

[12] L. Sweeney. 2002. k-anonymity: A Model for Protecting Privacy. International

Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2002).

[13] C. Timberg and D. Harwell. 2020. Government Efforts to Track Virus Through

Phone Location Data Complicated by Privacy Concerns. The Washiongton

Post (2020). https://www.washingtonpost.com/technology/2020/03/19/privacycoronavirus-phone-data/

[14] C. Timberg, D. Harwell, and A. Safarpour. 2020. Most Americans Are not

Willing or Able to Use an App Tracking Coronavirus Infections. That’s a

Problem for Big Tech’s Plan to Slow the Pandemic. Retrieved Dec 11, 2020

from https://www.washingtonpost.com/technology/2020/04/29/most-americansare-not-willing-or-able-use-an-app-tracking-coronavirus-infections-thatsproblem-big-techs-plan-slow-pandemic/

[15] C. Troncoso, M. Payer, J. Hubaux, M. Salathé, J. Larus, et al. 2020. Decentralized

Privacy-preserving Proximity Tracing. arXiv preprint arXiv:2005.12273 (2020).
Function Secret Sharing for PSI-CA: With Applications to Private Contact Tracing

Steve Lu, Stealth Software Technologies, Inc., steve@stealthsoftwareinc.com, CEO

We present a new privacy-preserving framework for proximity/risk aware contact tracing based on new 

cryptographic constructions. In this work, we describe a token-based solution to contact tracing via 

Distributed Point Functions (DPF) and, more generally, Function Secret Sharing (FSS). The key idea 

behind the solution is that FSS natively supports secure keyword/token search on raw sets of keywords 

without a need for processing the keyword sets via a data structure for set membership. Furthermore, 

the FSS functionality features a linear "homomorphic encryption" property which enables aggregating 

up numerical payloads associated with multiple matches without additional interaction between phones 

and servers. Combined with new models to represent proximity/risk as these numerically aggregated 

values and a secure process from phone-to-server, these features combined make an attractive tool for 

lightweight privacy-preserving searching on a database of tokens belonging to infected individuals.
Modelling multipath interference for BLE

proximity detection and exposure scoring

Dr Ramsey Faragher,

CEO Focal Point Positioning,

Fellow in Computer Science, Queens’ College, University of Cambridge

This talk will cover the development of, and some results from, a physics-based BLE signal

simulator that can accurately model the impact of multipath interference on BLE signal strength

measurements. This is one of the major sources of error for BLE measurements, impacting the

accuracy of proximity and exposure measurements, and leading to false positives and false

negatives. The talk covers findings from the simulations that can improve contact tracing apps.

The author is an expert in this field, with almost 1000 citations on the topic of BLE multipath

interference and its impact on distance and positioning measurements.

The presentation will cover:

● How the simulator models multipath interference for BLE signals

● Experimental validation of the simulator’s accuracy using real-world datasets

● Findings of the simulator when testing GAEN settings used by smartphones today

● Recommendations for improving BLE-based proximity detection

Experimental validation was performed by comparing the simulator output to real test data from

both University of Cambridge and Trinity College Dublin. These confirm that the simulator can

reproduce key signal behaviours and power variations that are measured on real tests.

The simulator can rapidly produce tens of thousands of realistic datasets in a few seconds that

would take tens of years to experimentally gather in a range of different interior layouts and

building materials (metal, wood, plastic, etc). This allows for rich sensitivity analyses to be

performed to determine the best settings for contact tracing apps using BLE (i.e. sampling rates,

number of measurements per exposure metrics, exposure thresholds, etc) when considering the

variations in signal power at various ranges in various environments due to multipath

interference. We can therefore investigate the key factors in reducing false positives and false

negatives. An example analysis of BLE settings for a dynamic covid exposure will be discussed,

The simulator is used to demonstrate false positive and false negative rates expected when

using three different measurement rates for BLE contact tracing apps - 15 seconds (original

NHSx app approach), 2.5 minutes (GAEN highest rate) and 5 minutes (GAEN lowest rate). The

simulator produces similar findings on false positive and false negative rates to those that were

published by the NHSx team in a BBC news article

(https://www.bbc.co.uk/news/technology-53765240), again confirming the ability of simulations

to help to assess the real-world behaviours of BLE based proximity detection schemes.

The talk will conclude with some recommendations on how best to use BLE in contact tracing

apps, based on these studies and the findings from the simulator
NIST event: Challenges for Digital Proximity Detection in Pandemics: Privacy, Accuracy, and Impact

Area: Epidemiological modeling of the efficacy of electronic proximity detection

Technical Abstract 

COSMOS Testbed – Proximity Detection and Social Distancing Estimation in COVID-19 Pandemic

Corresponding Author: Prof. Zoran Kostic, zk2172@columbia.edu

Authors; Mahshid Ghasemi, Zhengye Yang, Hongzhe Ye, Zihao Xiong , Mingfei Sun, Mingei Sun, Zoran 

Kostic, Javad Ghaderi, Gil Zussman. Electrical Engineering Dept., Columbia University, New York.

Smart-city intersections will play a crucial role in automated traffic management and improvement in 

pedestrian safety in cities of the future. They are also a source of information about pedestrian density 

and behavior, data which can be critical in assessing and controlling pandemics such as COVID-19.

The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment 

(COSMOS) which is being built in Harlem-New York, enables research on technologies supporting smart 

cities. It deploys a variety of infrastructure sensors, including street-level and bird’s eye cameras, whose 

data is aggregated and processed by AI-enabled servers. COSMOS pilot node at the intersection of 

Amsterdam Avenue and 120th street has been used to experiment with video-based applications focused 

on pedestrian detection, tracking, density estimation, and corresponding data processing. We have used 

this capability to collect and process data about pedestrians during the COVID-19 pandemic, and to 

understand their behavior.

Two video-based approaches to monitoring pedestrians in a traffic intersection have been developed 

– one based on street-level cameras and the other one based on bird’s eye’s cameras. We designed fully 

automated multi-stage social distancing analyzer pipelines, which utilize: (i) customized deep-learning 

based object detection, (ii) high accuracy object tracking algorithms, and (iii) original methods for 

assessing the proximity of pedestrians and the likelihood of belonging to safe social groups. Whereas the 

goals of the two approaches and technical problems are the same, a number of details provide different 

challenges in the domains of object detection and tracking, and social distancing analysis. Bird’s eye 

methodology provides inherent privacy preserving features.

Video data of the pilot intersection collected during many months prior and during the pandemic has 

been processed using the two pipelines. We have extracted the statistics in form of pedestrian proximity 

histograms, time-variant changes, and social distancing groups. The obtained results quantify the 

intuitively-expected changes in pedestrian behavior pre-pandemic and during the pandemic. The obtained 

detection accuracies are respectable for social distancing applications. New insights in the behavior of 

social groups have been gained. Further development of the pipelines can facilitate not only real time 

assessment of pandemic-related situations in dense metropolitan areas, but also means for alarming the 

pedestrians and authorities in case of overcrowding which is unacceptable from the perspective of 

managing of the pandemic.
Title: A Simplistic Machine Learning Approach to Contact Tracing

Presenters:

Carlos Gómez, ML-Labs, Technological University Dublin, carlos.g.tapia@myTUDublin.ie

Niamh Belton, ML-Labs, University College Dublin, niamh.belton@ucdconnect.ie

Boi Quach, ML-Labs, Dublin City University, mai.quach3@mail.dcu.ie

Jack Nicholls, ML-Labs, University College Dublin, jack.nicholls@ucdconnect.ie

Devanshu Anand, ML-Labs, Dublin City University, devanshu.anand2@mail.dcu.ie

Target Area: Machine Learning Algorithms

The SFI Centre for Machine Learning (ML-Labs) arranged teams of ML-Labs PhD students to take part 

in the Too Close for Too Long (TC4TL) challenge organised by NIST and PACT. The objective of the 

challenge was to calculate the distance between two phones given phone instrumental data. By hand 

crafting features and employing either a Multi-Layer Perceptron (MLP) or a Gradient Boosting Machine 

(GBM), our supervised classification method resulted in state-of-the-art performance.

We harnessed the data provided by NIST of almost 25,000 event files, where each event was the data 

collected between two devices over a period of time. An event file was accompanied by a ground truth 

maximum distance label. The data collected included Receiver Strength Signal Indicator (RSSI) values 

and data collected from other instruments such as accelerometers and gyroscopes. 

The most significant finding of the project was a distance formula for estimating the distance between 

two devices using Bluetooth data. This is a known formula in the domain. Given two phones, it is 

possible to compute a distance estimate d' using equation (1) where RSSI is the RSSI signal measured 

by the receiving phone, N depends on environmental factors ranging from 2 (good conditions) to 4 

(bad conditions) and measured Power (TX) indicates the 1 meter RSSI. The optimal values of TX and N 

for fine and coarse grain events were found using a greedy search. 

 (1)

We built two models, an MLP and a GBM that were trained on the predicted distance feature and 

additional categorical features such as the transmitter and receiver device. Both models were tuned 

and were evaluated using the software provided by NIST. The results are shown in the table below.

Fine Grain 

1.2 m

Fine Grain 

1.8m

Fine Grain 

3m

Coarse Grain 

1.8m

Average nDCF

GBM (proposed) 0.6 0.52 0.58 0.37 0.5175

MLP (proposed) 0.61 0.48 0.55 0.44 0.52

Contact-TracingProject0.68 0.54 0.59 0.41 0.555

LCD 0.6 0.58 0.63 0.55 0.59

Given the significant architectural differences of the MLP and GBM, these results demonstrate how a 

supervised classification technique trained on the aforementioned handcrafted features is a wellfounded approach to the problem. In addition to achieving state-of-the-art performance, the training 

time for both models is less than thirty seconds on a CPU. The success of this simplistic approach can 

be mainly attributed to the 'predicted distance' variable which is based on received RSSI signals. Both 

models saw a drastic performance improvement when adding this handcrafted feature. This feature 

interacted with variables relating to the transmitter and receiver devices to further improve the 

accuracy of the model. This highlights how a fusion of machine learning and domain knowledge can 

result in superior performance.

NIST Event: Challenges for Digital Proximity Detection in Pandemics: Privacy, Accuracy, and Impact

The Feasibility of Co-location Detection through a Deep Learning Fusion of Mobile Sensors

Sheshank Shankar

PathCheck Foundation

sheshank.shankar@pathcheck.org

In this talk, we discuss the feasibility of effective proximity sensing through deep

learning models trained on temporal Bluetooth Low Energy (BLE) and mobile sensor signals.

This is in the context of the PathCheck team’s submission to the NIST Too Close for Too Long

(TC4TL) Challenge. Provided two datasets (further referred to as the TRAIN and TEST datasets)

containing the mobile sensors logs from multiple mobile co-location experiments, we trained a

temporal one-dimensional convolutional network. This model achieved an average normalized

decision cost function (nDCF) value of 0.6425, placing 3rd in the challenge.

A major challenge we encountered across our experiments is the lack of generalization in

deep learning predictors. Since the TRAIN and TEST datasets were collected through the same

protocol by different organizations in different environments, accurate predictions on both

datasets are a good sign of a generalizable model. Even with complex architectures (such as

LSTM, GRU, CNN/RNN hybrids) and data representations, models are not able to achieve a

high level of accuracy on the other dataset. To assess the generalizability of the datasets, we

switch the TRAIN and TEST datasets, but it does not yield any notable improvement in

generalization. Informed by these results, we try to estimate the gap between the two datasets.

The inconsistencies between the two distributions can be observed by the high values of l2

between the pairwise distances of any two feature vectors. Looking at the cross-dataset nearest

neighbour pairs, we find that a significant number of the pairs had significantly different

distances. Further proving this data discrepancy, we receive similar results when training on an

optimal training subset that includes only the 2 nearest points in the training dataset for each

point in the test dataset is included. We also find that the average distance between a

nearest-neighbor pair is fairy low; however, if we isolate nearest-neighbor pairs with different

classes, the average distance is around 8 times as much! This implies that the two data

distributions (although collected through the same protocol) are not similar enough to capture

any generalizable information. A thorough statistical analysis would be needed to confirm this

argument, as highly non-linear manifolds that can fit both distributions may exist.

To better understand the potential for an accurate predictor in these two data

distributions, we performed low dimensional projections of the TRAIN and TEST datasets to

identify underlying target class clusters in the feature space. These plots showed heavily

overlapping clusters, without a clear decision boundary between any two distance buckets (1.2,

1.5, 3, 4 meters). We suspect that this could be caused by external factors such as, physical

barriers present between phones, the number and time spread of observed chirps, and multi-path

signals reflected from surfaces (e.g. indoor vs outdoor). It is, however, possible that higher

dimensional hyper-planes dividing the classes exist. Lastly, we briefly discuss potential solutions

to these challenges, including hybrids of other co-location modalities.

Further Reference: S. Shankar, R. Kanaparti, A. Chopra, R. Sukumaran, P. Patwa, M. Kang, A.

Singh, K. McPherson, R. Raskar. Proximity Sensing: Modeling and Understanding Noisy

RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing, 2020.
Presenter: Nicholas Maynard

Affiliation: The MITRE Corporation

E-mail address: nmaynard@mitre.org

Title: Entropy Based Discretization’s and Weight Optimization for Configuring the GAEN system.

Abstract:

The Google-Apple Exposure Notification (GAEN) system, currently in use aims to provide a meaningful 

improvement to the failures of manual contact tracing. Namely it allows a public health authority (PHA)

to take advantage of Bluetooth signals emitted from cell phones to determine if an exposure occurred 

rather than relying on the memory of the infected individuals. In addition to this capability, the GAEN 

system is privacy preserving for the individuals who decide to opt into it.

While this tool has the capability to enhance manual contact tracing, it currently requires the setting of a 

configuration file which consists mostly of sensor thresholds/weights for the bins created by these 

thresholds. In this regard, there are limited tools available for setting a configuration file other than 

opening notepad and looking through the file itself to make the change. Additionally, there is limited 

insight in how to translate guidelines set by a public health authority (say less than 6ft for 15 minutes) 

into the most ideal corresponding configuration values. This is where we step in in an attempt to bolster 

the tools available for setting a configuration file, as well as assist a PHA in making informed decisions to 

best approximate their definition of too close for too long.

In this talk, we will first present the current data available for use as pulled from the datasets created by 

Mark Krangle. Next, we will go into the overall approach/tools built for configuring GAEN to a PHA’s 

needs based on real sensor data. More specifically we will detail how a PHA may translate tracing based 

on time spent in various distance ranges into proper sensor values through a method called entropybased discretization’s. Additionally, we will detail an optimization approach taken to set the weights of 

the attenuation bins based on simulated interactions sampled from the datasets generated by Mark 

Krangle. Lastly, we will run through an example from start to finish using said tools suite to get an idea 

of how a PHA would interact with the tools developed and get an idea of what sort of false positive/false 

negative rates they could expect.

With the ability to make informed sensor settings a PHA should be able to take full advantage of the 

GAEN system in making accurate predictions of risky interactions and hopefully use the knowledge to 

inform the people who need it the most.
Presenter: Brian Thompson

Affiliation: The MITRE Corporation

E-mail address: bthompson@mitre.org

Title: Efficacy of Current Approaches and An Alternative Paradigm for Digital Contact Tracing

Abstract:

The Google-Apple Exposure Notification (GAEN) system, currently being used by at least 33 countries 

and 17 U.S. states and territories, aims to complement and overcome some of the shortfalls of manual 

contact tracing. However, evidence indicating the effectiveness of GAEN, in terms of reducing the 

number of new infections, has been minimal, even after taking low uptake rates into account. As more 

countries and states are making decisions about whether to invest resources into pushing out GAEN to 

their constituents, understanding its potential impact as well as its limitations is crucial.

A common approach to measure the performance of a digital contact tracing mechanism is by its ability 

to detect “close contacts”, typically based on the WHO or CDC’s definitions. By those definitions, most 

contacts do not lead to infection — estimates from the literature suggest a false alarm rate upwards of 

90%, and likely significantly higher. Such a high rate of notifications to uninfected users is likely to drive 

down app usage and compliance. GAEN aims to make more nuanced determinations of exposure risk by 

leveraging a granularity of data that is not available to manual contact tracers, but its accuracy at 

notifying only infected users does not seem to be adequately assessed by existing analyses. Studies that 

examine the impact of digital contact tracing on population-level health outcomes often assume high 

compliance of app users with quarantine recommendations and do not consider the consequences of a 

high false alarm rate on compliance.

In this talk, we will first present the results of a modeling study that explores the efficacy of digital 

contact tracing using simulated interactions between infected and susceptible individuals carrying 

GAEN-enabled mobile devices. A key finding is that, although improvements in the accuracy of proximity 

estimation methods can increase the efficacy of digital contact tracing, even perfect estimators cannot 

yield a high detection rate of secondary cases without also having a high false alarm rate, which limits 

the potential impact of such technologies.

Finally, we propose an alternative paradigm for digital contact tracing that bootstraps the GAEN 

framework to detect and mitigate the effect of super-spreading events on viral spread using “lateral”

rather than “forward” contact tracing. Our proposed approach has the potential to overcome 

fundamental limitations on the efficacy of the current paradigm, can have impact at lower adoption 

rates, and works even if the infected person does not have the app. Mitigation strategies that include 

manual contact tracing, the current approach to digital contact tracing implemented by GAEN, and our 

proposed paradigm for detecting super-spreading events may present the best opportunity to finally 

control the COVID-19 pandemic.