A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning-Reference-Cited by-同舟云学术

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A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning

Published:2021-05-20 Issue:3 Volume:25 Page:1153-1175
ISSN:1361-1682
Container-title:Transactions in GIS
language:en
Short-container-title:Transactions in GIS

Author:

Rao Jinmeng¹^ORCID,Gao Song¹^ORCID,Li Mingxiao¹²,Huang Qunying³^ORCID

Affiliation:

1. Geospatial Data Science Lab Department of Geography University of Wisconsin‐Madison Madison WI USA

2. Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University Shenzhen China

3. Spatial Computing and Data Mining Lab Department of Geography University of Wisconsin‐Madison Madison WI USA

Funder

Wisconsin Alumni Research Foundation

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12769

Reference73 articles.

1. Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data

2. Geographic Information Technologies and Personal Privacy

3. Recommendations in location-based social networks: a survey

4. Security and privacy for geospatial data

5. Understanding Distributed Poisoning Attack in Federated Learning

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2. On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper);ACM Transactions on Spatial Algorithms and Systems;2024-06-30

3. MobilityDL: a review of deep learning from trajectory data;GeoInformatica;2024-05-28

4. Practical and privacy-preserving geo-social-based POI recommendation;Journal of Information and Intelligence;2024-03

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