Prediction of Mobile App Privacy Preferences with User Profiles via Federated Learning-Reference-Cited by-同舟云学术

Prediction of Mobile App Privacy Preferences with User Profiles via Federated Learning

Published:2022-04-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy
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Author:

Brandão André¹,Mendes Ricardo²,Vilela João P.¹

Affiliation:

1. University of Porto, Porto, Portugal

2. University of Coimbra, Coimbra, Portugal

Funder

MIT Portugal

Fundação para a Ciência e a Tecnologia

Ngi trust

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3508398.3511526

Reference28 articles.

1. ProtectMyPrivacy

2. Studying users’ adaptation to Android’s run-time fine-grained access control system

3. André Brand ao, Ricardo Mendes , and Jo ao P Vilela . 2021. Efficient privacy preserving distributed K-means for non-IID data . In Advances in Intelligent Data Analysis XIX. Springer International Publishing , Cham , 439--451. André Brand ao, Ricardo Mendes, and Jo ao P Vilela. 2021. Efficient privacy preserving distributed K-means for non-IID data. In Advances in Intelligent Data Analysis XIX. Springer International Publishing, Cham, 439--451.

4. Who Moved My Data? Privacy Protection in Smartphones

5. Android permissions

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