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

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

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...

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Bibliographic Details
Main Authors: Murakami Takao, Hamada Koki, Kawamoto Yusuke, Hatano Takuma
Format: Article
Language:English
Published: Sciendo 2021-04-01
Series:Proceedings on Privacy Enhancing Technologies
Subjects:
location privacy
location synthesizer
cluster-specific feature
multiple tensor factorization
Online Access:https://doi.org/10.2478/popets-2021-0015

Internet

https://doi.org/10.2478/popets-2021-0015

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