A multimodal differential privacy framework based on fusion representation learning
Author:
Affiliation:
1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
2. School of Information and Communication Technology, Griffith University, Nathan, Australia
Funder
Key-Area Research and Development Program of Guangdong Province
the Science and Technology Program of Guangzhou, China
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2111406
Reference32 articles.
1. Differentially Private Histogram Publishing through Lossy Compression
2. Multimodal fusion for multimedia analysis: a survey
3. Multimodal Machine Learning: A Survey and Taxonomy
4. CGM
5. IEMOCAP: interactive emotional dyadic motion capture database
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1. Lightweight and Efficient Privacy-Preserving Multimodal Representation Inference via Fully Homomorphic Encryption;Intelligent Information and Database Systems;2023
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