On the utility of differentially private synthetic data generation and differentially private model release
碩士 === 國立中興大學 === 資訊科學與工程學系所 === 107 === In recent years, with the widespread use of neural networks, a large amount of personal data is being collected. To protect the model from leaking private information, it combines with differential privacy to achieve the goal. In our work, we use the followin...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394031%22.&searchmode=basic |