  In this work, we initiate the study of human mobility from sanitized call
detail records (CDRs). Such data can be extremely valuable to solve important
societal issues such as the improvement of urban transportation or the
understanding on the spread of diseases. One of the fundamental building block
for such study is the computation of mobility patterns summarizing how
individuals move during a given period from one area e.g., cellular tower or
administrative district) to another. However, such knowledge cannot be
published directly as it has been demonstrated that the access to this type of
data enable the (re-)identification of individuals. To answer this issue and to
foster the development of such applications in a privacy-preserving manner, we
propose in this paper a novel approach in which CDRs are summarized under the
form of a differentially-private Bloom filter for the purpose of privately
counting the number of mobile service users moving from one area (region) to
another in a given time frame. Our sanitization method is both time and space
efficient, and ensures differential privacy while solving the shortcomings of a
solution recently proposed to this problem. We also report on experiments
conducted with the proposed solution using a real life CDRs dataset. The
results obtained show that our method achieves - in most cases - a performance
similar to another method (linear counting sketch) that does not provide any
privacy guarantees. Thus, we conclude that our method maintains a high utility
while providing strong privacy guarantees.
