Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

• Published in: Proceedings on Privacy Enhancing Technologies 2021-04, Vol.2021 (2), p.5-26
• Main Authors: Murakami, Takao, Hamada, Koki, Kawamoto, Yusuke, Hatano, Takuma
• Format: Article
• Language: English
• Subjects: cluster-specific feature; location privacy; location synthesizer; multiple tensor factorization
• ISSN: 2299-0984; 2299-0984
• DOI: 10.2478/popets-2021-0015

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 su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called . 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.