A Novel Location Privacy Protection Scheme with Generative Adversarial Network

Wang, Wenxi; Zhang, Weilong; Zhang, Jin; Sun, Keyan; Zou, Runlin; Huang, Chenrong; Tian, Yuan

doi:10.1007/978-981-15-7530-3_2

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Another approach used in LBS schemes is one that is decentralised (Lv et al, 2020) or user-centric and depends on users to protect their privacy themselves instead of using a TTP. Different collaborative-based schemes have been proposed based on this concept Shokri et al, 2014;Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Trustworthy collaborative trajectory scheme for continuous LBS

Tian¹,

Song²,

Murad³

et al. 2022

IJSNET

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With the high demand for using location-based services (LBSs) in our daily lives, the privacy protection of users' trajectories has become a major concern. When users utilise LBSs, their location and trajectory information may expose their identities in continuous LBSs. Using the spatial and temporal correspondences on users' trajectories, adversaries can easily gather their private information. Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries. However, there is an assumption of a trusting relationship between peers. In this paper, we propose a trustworthy collaborative trajectory privacy (TCTP) scheme, which anonymises users' trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs. Moreover, the TCTP scheme provides query content preservation based on a fake query concept in which we conceal the user's actual query among a set of queries. The results of several experiments with different conditions confirm that our proposed scheme can protect users' trajectory privacy successfully in a trustworthy and efficient manner.

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Section: Introductionmentioning

confidence: 99%