  With the widespread use of LBSs (Location-based Services), synthesizing
location traces plays an increasingly important role in analyzing spatial big
data while protecting user privacy. In particular, a synthetic trace that
preserves a feature specific to a cluster of users (e.g., those who commute by
train, those who go shopping) is important for various geo-data analysis tasks
and for providing a synthetic location dataset. Although location synthesizers
have been widely studied, existing synthesizers do not provide sufficient
utility, privacy, or scalability, hence are not practical for large-scale
location traces. To overcome this issue, we propose a novel location
synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We
model various statistical features of the original traces by a transition-count
tensor and a visit-count tensor. We factorize these two tensors simultaneously
via multiple tensor factorization, and train factor matrices via posterior
sampling. Then we synthesize traces from reconstructed tensors, and perform a
plausible deniability test for a synthetic trace. We comprehensively evaluate
PPMTF using two datasets. Our experimental results show that PPMTF preserves
various statistical features including cluster-specific features, protects user
privacy, and synthesizes large-scale location traces in practical time. PPMTF
also significantly outperforms the state-of-the-art methods in terms of utility
and scalability at the same level of privacy.
