The document proposes a privacy-preserving app for tracking contagious diseases. It works by having users install an app that tracks their location locally on their phone. When a new case is reported, health authorities upload the infected person's location history. The app then computes if and when the user may have come into contact with the infected person based on overlapping locations, while keeping all location data private. The goal is to notify potential exposures for informed decisions without surveilling individuals.

1. Privacy-Preserving
Contagious Disease Tracking
Carsten Kessler Hunter College, City University of New York
http://carsten.io @carstenkessler

2. http://nyti.ms/1FKHUXJ

3. http://nyti.ms/1tsbdcg

8. • Information about an individual's location is very
sensitive, especially when constantly tracked and in
a health context
• Goal: Offer an information service that allows users
to understand their potential exposure to a disease
and make informed decisions – not tracking down
potential patients
Preserving users' privacy

9. • User installs app and can forget about it
• Location history is recorded directly on user's phone
• This record of an individual's whereabouts
never leave their phone
• Any computation is performed directly on the phone
Solution

10. Pathogion
Path of contagion
* Thanks to Hunter’s Undergraduate Research Initiative!

11. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

12. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

13. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

14. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

15. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

16. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification

17. 1 User installs
app, enables
location
tracking
2 tracks location, saves in local db
3 New case: health
authorities reconstruct
patient’s path for last days
4 Upload reconstructed
patient track to
notification server
5 Notification server
pushes patient track
to app
6 Computing spatial-
temporal intersections
7 User gets a notification
if potential matches have been
found, showing details about
the patient’s path for verification
Icons by iconmonstr

19. • Location APIs never allow complete anonymity,
unless reduced to pure GPS positioning
• Location history may become very large, need to
limited time frame and “thin out” data
• Computation of potential meeting points can be
done on phone, but need further optimization
Drawbacks

20. • Currently implementing “push” of patient tracks
through Amazon Simple Notification Service
• Intersection algorithm needs more performance
tuning
• Consider other use cases, such as looking for
witnesses of felonies
• Funding, anyone?
What’s next…

21. Thank you!
Carsten Kessler Hunter College, City University of New York
http://carsten.io @carstenkessler
Photo by Mario Sixtus.