Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Author:
Affiliation:
1. National University of Singapore, Singapore, Singapore
2. Qilu University of Technology (Shandong Artificial Intelligence Institute), Jinan, China
3. Tianjin University of Technology, Tianjin, China
4. Shandong University, Jinan, China
Funder
National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative
Young creative team in universities of Shandong Province
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3477495.3532044
Reference49 articles.
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3. Vincent Bindschaedler , Reza Shokri , and Carl A. Gunter . 2017. Plausible Deniability for Privacy-Preserving Data Synthesis . Proc. VLDB Endow. ( 2017 ), 481--492. Vincent Bindschaedler, Reza Shokri, and Carl A. Gunter. 2017. Plausible Deniability for Privacy-Preserving Data Synthesis. Proc. VLDB Endow. (2017), 481--492.
4. Xiaoyan Cai , Junwei Han , and Libin Yang . 2018. Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation . In AAAI. AAAI Press , 5747--5754. Xiaoyan Cai, Junwei Han, and Libin Yang. 2018. Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation. In AAAI. AAAI Press, 5747--5754.
5. CFGAN
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