Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation-Reference-Cited by-同舟云学术

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Chen Chaochao¹,Wu Huiwen²,Su Jiajie¹,Lyu Lingjuan³,Zheng Xiaolin⁴,Wang Li²

Affiliation:

1. Zhejiang University, China

2. Ant Group, China

3. Sony AI, China

4. Zhejiang University, China and JZTData Technology, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512192

Reference37 articles.

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2. Rajendra Bhatia. 2007. Perturbation bounds for matrix eigenvalues. SIAM. Rajendra Bhatia. 2007. Perturbation bounds for matrix eigenvalues. SIAM.

3. The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy

4. Decoding by Linear Programming

5. Chaochao Chen Liang Li Bingzhe Wu Cheng Hong Li Wang and Jun Zhou. 2020. Secure Social Recommendation Based on Secret Sharing. In ECAI. 506–512. Chaochao Chen Liang Li Bingzhe Wu Cheng Hong Li Wang and Jun Zhou. 2020. Secure Social Recommendation Based on Secret Sharing. In ECAI. 506–512.

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