Privacy-Preserving Fair Item Ranking

Author:

Sun Jia Ao,Pentyala Sikha,Cock Martine De,Farnadi Golnoosh

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Ammad-Ud-Din, M., et al.: Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019)

2. Araki, T., Furukawa, J., Lindell, Y., Nof, A., Ohara, K.: High-throughput semi-honest secure three-party computation with an honest majority. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 805–817 (2016)

3. Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 405–414 (2018)

4. Canny, J.: Collaborative filtering with privacy. In: IEEE Symposium on Security and Privacy, pp. 45–57 (2002)

5. Celis, L.E., Straszak, D., Vishnoi, N.K.: Ranking with fairness constraints. In: 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), vol. 107, pp. 28:1–28:15 (2018)

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