Presentation at NYC Department of Health and Mental Hygiene GIS Day, November 30, 2015

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.