Yves-Alexandre de Montjoye

1. Big but personal
(meta)data
How Human Behavior Bounds Privacy
and What We Can We Do About It
Yves-Alexandre de Montjoye
@yvesalexandre
MIT Media Lab

7. 12 points

13. Is the way you move
around
as unique as
your fingerprint

14. We can use points to
identify a fingerprint

15. Scott

16. From 10 to 11am
1 km²
1 point for mobility data
~

17. 2 points
Around 11:30am

18. 3 points
For lunch

19. Boston

20. How many points do I need
to uniquely identify a
mobility traces?

21. De-identification

23. Entire country of 1.5 millions people
Our behavior is unique enough
4 points
Identify 95% of people
de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013).
Unique in the Crowd: The privacy bounds of human mobility. Nature SRep, 3.

24. What it means
1. It is possible to re-identify mobile phone
metadata (even if there is no name or phone
number)

25. Resolution: 800 pixels

26. Resolution: 300 pixels

27. Resolution: 150 pixels

28. Resolution: 75 pixels

29. Resolution: 30 pixels

30. Where’s Thierry ?

31. ?

32. 4pm – 10pm7pm-8pm

33. Estimating Privacy
Spatial resolution
Temporal resolution
Number of points
de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013).
Unique in the Crowd: The privacy bounds of human mobility. Nature SRep, 3.

34. Harder to find people
Much easier to find people
Harder to find people

36. What it means
1. It is possible to re-identify mobile phone
metadata (even if there is no name or phone
number)
2. It is not simply a question of coarsening the
data (we’d just need a few more points)

37. BFI: Personality test

38. BFI: Personality test

39. Behavioral indicators derived from
metadata using the Bandicoot toolbox

40. Predicting personality
using metadata
de Montjoye, Y. A., Quoidbach, J., Robic, F., & Pentland, A. S.
(2013). Predicting personality using novel mobile phone-based
metrics. In Social Computing, Behavioral-Cultural Modeling and
Prediction (pp. 48-55). Springer Berlin Heidelberg.

41. What it means
1. It is possible to re-identify mobile phone
metadata (even if there is no name or phone
number)
2. It is not simply a question of coarsening the
data (we’d just need a few more points)
3. It is not “just” metadata or what is directly
visible in the data (e.g. one might use it to
predict your personality)

42. Eagle, N., de Montjoye, Y-A.., &
Bettencourt, L. M. (2009). Community
computing: Comparisons between rural
and urban societies using mobile
phone data. IEEE Computational
Science and Engineering
We should use this data
Deville, P. et al. (2014). Dynamic
population mapping using mobile
phone data. Proceedings of the
National Academy of Sciences,
201408439.
Wesolowski, A., Eagle, N., Tatem, A. J.,
Smith, D. L., Noor, A. M., Snow, R. W., &
Buckee, C. O. (2012). Quantifying the
impact of human mobility on malaria.
Science, 338(6104), 267-270.

43. (but in a privacy-conscientious way)
We should use this data
by:
understanding what the real risks are
and
designing solutions

44. Privacy-conscientious anonymization
de Montjoye, Y. A., Smoreda, Z., Trinquart, R., Ziemlicki, C., & Blondel, V.
D. (2014). D4D-Senegal: The Second Mobile Phone Data for
Development Challenge. arXiv preprint arXiv:1407.4885.
e.g. 2-week mobility traces
of 27 x 300.000 individuals
+ Bandicoot’s behavioral
indicators

45. Online systems: from privacy to
security
openPDS/SafeAnswers:
- Only shares answers,
not raw data
- Security mechanisms

46. openPDS/SafeAnswers
de Montjoye Y.-A., Wang S., Pentland A., On the Trusted Use of Large-
Scale Personal Data. IEEE Data Engineering Bulletin, 35-4 (2012).
de Montjoye, Y. A., Shmueli, E., Wang, S. S., & Pentland, A. S. (2014).
openPDS: Protecting the Privacy of Metadata through SafeAnswers.
PLoS ONE, 9(7), e98790.

47. Yves-Alexandre de Montjoye
MIT Media Lab
@yvesalexandre
http://deMontjoye.com
In collaboration with Alex “Sandy” Pentland, César Hidalgo, Vincent Blondel,
Cameron Kerry, Jake Kendall, Michel Verleysen, Erez Shmueli, Arek Stopczynski,
Sune Lehmann, Eaman Jahani