Open Access
| Issue |
SHS Web Conf.
Volume 220, 2025
2025 2nd International Conference on Language Research and Communication (ICLRC 2025)
|
|
|---|---|---|
| Article Number | 03015 | |
| Number of page(s) | 6 | |
| Section | Cultural Communication, Brand Marketing, and Media Strategies | |
| DOI | https://doi.org/10.1051/shsconf/202522003015 | |
| Published online | 13 August 2025 | |
- F. Eggers, F. T. Beke, P. C. Verhoef, J. E. Wieringa, The market for privacy: Understanding how consumers trade off privacy practices. J. Interact. Mark. 58, 341– 360 (2023) [Google Scholar]
- X. Meng, S. Wang, K. Shu, J. Li, B. Chen, H. Liu, Y. Zhang, Personalized privacypreserving social recommendation. Proc. 32nd AAAI Conf. Artif. Intell. (AAAI-18) (2018) [Google Scholar]
- Y. Guo, B. Liu, X. Chen, PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization. arXiv:2103.01548v2 (2021) [Google Scholar]
- M. Ammad-ud-din, E. Ivannikova, S. A. Khan, W. Oyomno, Q. Fu, K. E. Tan, A. Flanagan, Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System. arXiv:1901.09888v1 (2019) [Google Scholar]
- A. Salemi, H. Zamani, Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models. arXiv:2409.09510v1 (2024) [Google Scholar]
- Y. Wang, X. Chen, D. Simchi-Levi, Privacy-Preserving Dynamic Personalized Pricing with Demand Learning. arXiv:2009.12920v2 (2021) [Google Scholar]
- B. Grandhi, N. Patwa, K. Saleem, Data-driven marketing for growth and profitability. EuroMed J. Bus. 15, 383-403 (2020) [Google Scholar]
- A. Bietti, C. Y. Wei, M. Dudík, J. Langford, Z. S. Wu, Personalization Improves Privacy–Accuracy Tradeoffs in Federated Learning. Proc. 39th Int. Conf. Mach. Learn. PMLR 162, 1396-1406 (2022) [Google Scholar]
- C. Lending, K. Minnick, P. J. Schorno, Corporate Governance, Social Responsibility, and Data Breaches. Financ. Rev. 53, 413-455 (2018) [Google Scholar]
- C. Wu, F. Wu, L. Lyu, T. Qi, Y. Huang, X. Xie, A federated graph neural network framework for privacy-preserving personalization. Nat. Commun. 13, 3091 (2022) [Google Scholar]
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