Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
Authors: Takao Murakami (AIST), Koki Hamada (NTT/RIKEN), Yusuke Kawamoto (AIST), Takuma Hatano (NSSOL)
Volume: 2021
Issue: 2
Pages: 5–26
DOI: https://doi.org/10.2478/popets-2021-0015
Abstract: With the widespread use of LBSs (Locationbased 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 largescale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (PrivacyPreserving 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-theart methods in terms of utility and scalability at the same level of privacy.
Keywords: location privacy, location synthesizer, cluster-specific feature, multiple tensor factorization
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