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. It has been recently used to understand the use of metadata by the NSA


In collaboration with César Hidalgo, Vincent Blondel, and Michel Verleysen

openPDS: Protecting the Privacy of Personal Data

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? The coming changes in data ownership open up a new way to protect the privacy of personal data. openPDS permits sensitive data processing to take place within a user’s PDS. The dimensionality of the data can be reduced on-the-fly to certified answers before being anonymously shared. This allows companies to offer innovative data-enabled services and products.


In collaboration with Samuel Wang, Erez Shmueli, Sandy Pentland, and the Harvard Berkman Center

What Can Your Phone Tell About You?

How much can one know about your personality just by looking at the way you use your phone? We provide the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users' personality. As mobile cellular subscriptions have hit 6 billion throughout the world, our method enable cost-effective, questionnaire-free investigation of personality-related questions at a large-scale.


In collaboration with Jordi Quoidbach, Florent Robic, and Sandy Pentland

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