Projects
The privacy bounds of human mobility
We used 15 months of data from 1.5 million people to show that 4 points--approximate places and times--are enough to identify 95% of individuals in a mobility database. Our work shows that human behavior puts fundamental natural constraints to the privacy of individuals and these constraints hold even when the resolution of the dataset is low; even coarse datasets provide little anonymity. We further developed a formula to estimate the uniqueness of human mobility traces. These findings have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
In collaboration with César Hidalgo, Vincent Blondel, and Michel Verleysen
Related and selected press:
- de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M. & Blondel, V.D. Unique in the Crowd: The privacy bounds of human mobility. Nature srep. 3, 1376; DOI:10.1038/srep01376 (2013).
- Artworks and visuals (.zip).
- In March 2013:
BBC News,
CNN,
World Economic Forum,
Wall Street Journal,
New-York Times,
Le Monde,
MIT Technology Review,
United Nations - Global Pulse,
Wired (US),
Wired (UK),
MIT News,
Boing Boing,
Fast Company,
PopSci,
Bruce Schneier,
GigaOM,
Phys.org,
Slate,
MIT Technology review,
Smithsonian
The Huffington Post,
The Telegraph,
The Inquirer,
Spiegel,
Die Welt
IEEE Spectrum
- Our editorial in the Christian Science Monitor: Solution to NSA overreach -- put people in charge of their own data.
- Our editorial in Le Monde: Il est temps de parler des métadonnées.
- In connection to the NSA revelations:
New-York Times,
Wall Street Journal CIO Journal,
Foreign Policy,
New Scientist,
MIT Technology Review,
The Atlantic,
Le Monde,
National Journal,
NBC,
Mashable,
Wired (JP),
TechDirt,
InternetActu (FR),
Privacy Commissioner Ontario, Canada
Mediapart (FR),
IEEE Spectrum,
openPDS/SaferAnswers: Protecting the Privacy of Metadata
In a world where sensors, data storage and processing power are too cheap to meter how do you ensure that users can realize the full value of their data while protecting their privacy? openPDS is a field-tested personal metadata management framework which allows individuals to collect, store, and give fine-grained access to their metadata to third parties. SafeAnswers is a new and practical way of protecting the privacy of metadata at individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. Together, openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata.
In collaboration with Samuel Wang, Erez Shmueli, Sandy Pentland, and the Harvard Berkman Center
Related and selected press:
- openPDS website
- de Montjoye, Y.-A., Wang S., Pentland A., On the Trusted Use of Large-Scale Personal Data. IEEE Data Engineering Bulletin, 35-4 (2012).
- openPDS privacy settings - Visuals (.zip).
- View our youtube video
- Press:
New-York Times,
Wall Street Journal,
Technology Review,
Le Monde (FR),
New Scientist,
GigaOM,
Scientific American,
The Edge,
Cisco,
Radio Canada,
New Scientist,
InternetActu (FR),
Les Echos (FR)
What Can Your Phone Metadata Tell About You?
How much can others learn about your personality just by looking at the way you use your phone? We provide the first evidence that personality types (for example, neurotism, extraversion, openness) can be predicted from standard mobile phone metadata. We have developed a set of novel psychology-informed indicators that can be computed from any set of mobile phone metadata. These fall into five categories, and range from the time it took you to answer a text, the entropy of your contacts, your daily distance traveled, or the percentage of text conversations you started. Using these 36 indicators, we were able to predict people's personalities correctely up to 63/%, 1.7 times better than random using only metadata.
In collaboration with Jordi Quoidbach, Florent Robic, and Sandy Pentland
Related and press:
- Click here for an infographic describing our research and results
- de Montjoye, Y.-A.*, Quoidbach J.*, Robic F.*, Pentland A., Predicting people personality using novel mobile phone-based metrics. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction, Washington, USA (2013)
- Press: Boston Globe,
Russia Today,
Le Monde (FR),
InternetActu (FR),
Mediapart (FR),
Agence Science Presse (FR),
Radio Canada (FR),
DefenseOne and Quartz,
The limits of community detection in networks
What can really be inferred from communities unfold by modularity-based algorithms? A broad and systematic characterization of the theoretical and practical performance of modularity contradicts the widely held assumption that the modularity function typically exhibits a clear global optimum. This implies that (i) modules identified via modularity maximization are not unique and should therefore be interpreted with extreme caution, and (ii) even moderate differences in modularity scores are meaningless.
In collaboration with Aaron Clauset, and Ben Good
Quantifying the Stability of Society
Is there such a thing as a 'poverty trap'? Logistic classifiers applied on communication and census data point to a new mechanism for poverty that relates to the persistence of relationships. This analysis shows that economic exchanges flow primarily through these persistent edges and the inability to maintain these ties can prevent upward economic mobility
In collaboration with Nathan Eagle and Aaron Clauset
Related:
- Stability in society: Parameters for the persistence of social networks, Master’s thesis, Université catholique de Louvain, 2009.
Modeling the Dynamics of Urbanization on Social Support Networks
What is attracting migrants to urban areas within the developing world? Using 4 years of movement and communication data, it is possible to model the reinforcing social mechanisms that could explain the recent rapid growth of urban areas.
In collaboration with Nathan Eagle and Luís M.A. Bettencourt
