FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning-Reference-Cited by-同舟云学术

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

Published:2023-11-13 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
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Author:

Park Chung¹^ORCID,Choi Taekyoon²^ORCID,Kim Taesan³^ORCID,Cho Mincheol³^ORCID,Hong Junui⁴^ORCID,Choi Minsung³^ORCID,Choo Jaegul⁵^ORCID

Affiliation:

1. SK Telecom / KAIST, Seoul / Daejeon, South Korea

2. Naver, Seongnam, South Korea

3. SK Telelcom, Seoul, South Korea

4. SK Telelcom / KAIST, Seoul / Daejeon, South Korea

5. Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP)

National Research Foundation of Korea

SK Telecom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589132.3625582

Reference26 articles.

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3. Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , and Ryan Rogers . 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 ( 2018 ). Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018).

4. PMF: A privacy-preserving human mobility prediction framework via federated learning;Feng Jie;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,2020

5. Identifying Human Mobility via Trajectory Embeddings