  The development of positioning technologies has resulted in an increasing
amount of mobility data being available. While bringing a lot of convenience to
people's life, such availability also raises serious concerns about privacy. In
this paper, we concentrate on one of the most sensitive information that can be
inferred from mobility data, namely social relationships. We propose a novel
social relation inference attack that relies on an advanced feature learning
technique to automatically summarize users' mobility features. Compared to
existing approaches, our attack is able to predict any two individuals' social
relation, and it does not require the adversary to have any prior knowledge on
existing social relations. These advantages significantly increase the
applicability of our attack and the scope of the privacy assessment. Extensive
experiments conducted on a large dataset demonstrate that our inference attack
is effective, and achieves between 13% to 20% improvement over the best
state-of-the-art scheme. We propose three defense mechanisms -- hiding,
replacement and generalization -- and evaluate their effectiveness for
mitigating the social link privacy risks stemming from mobility data sharing.
Our experimental results show that both hiding and replacement mechanisms
outperform generalization. Moreover, hiding and replacement achieve a
comparable trade-off between utility and privacy, the former preserving better
utility and the latter providing better privacy.
