Differentially Private Sparse Mapping for Privacy-Preserving Cross Domain Recommendation-Reference-Cited by-同舟云学术

Differentially Private Sparse Mapping for Privacy-Preserving Cross Domain Recommendation

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Weiming¹^ORCID,Zheng Xiaolin¹^ORCID,Chen Chaochao¹^ORCID,Hu Mengling¹^ORCID,Liao Xinting¹^ORCID,Wang Fan¹^ORCID,Tan Yanchao²^ORCID,Meng Dan³^ORCID,Wang Jun³^ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Fuzhou University, Fuzhou, China

3. OPPO Research Institute, Shenzhen, China

Funder

Leading Expert of ??Ten Thousands Talent Program?? of Zhejiang Province

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611924

Reference76 articles.

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4. The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy

5. Mathieu Blondel , Vivien Seguy , and Antoine Rolet . 2018 . Smooth and sparse optimal transport . In International conference on artificial intelligence and statistics. PMLR, 880--889 . Mathieu Blondel, Vivien Seguy, and Antoine Rolet. 2018. Smooth and sparse optimal transport. In International conference on artificial intelligence and statistics. PMLR, 880--889.

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