| 2022 | PRDC | Examining the Utility of Differentially Private Synthetic Data Generated using Variational Autoencoder with TensorFlow Privacy. | Bo-Chen Tai, Szu-Chuang Li, Yennun Huang, Pang-Chieh Wang |
| 2021 | PRDC | A VAE Conversion Method for Private Data Linkage. | Bo-Chen Tai, Szu-Chuang Li, Yennun Huang |
| 2019 | PRDC | Evaluating Variational Autoencoder as a Private Data Release Mechanism for Tabular Data. | Szu-Chuang Li, Bo-Chen Tai, Yennun Huang |
| 2018 | PRDC | Exploring the Relationship Between Dimensionality Reduction and Private Data Release. | Bo-Chen Tai, Szu-Chuang Li, Yennun Huang, Neeraj Suri, Pang-Chieh Wang |
| 2017 | AINA | Data-Driven Approach for Evaluating Risk of Disclosure and Utility in Differentially Private Data Release. | Kang-Cheng Chen, Chia-Mu Yu, Bo-Chen Tai, Szu-Chuang Li, Yao-Tung Tsou, Yennun Huang, Chia-Ming Lin |
| 2017 | AINA | K-Aggregation: Improving Accuracy for Differential Privacy Synthetic Dataset by Utilizing K-Anonymity Algorithm. | Bo-Chen Tai, Szu-Chuang Li, Yennun Huang |
| 2017 | PRDC | Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy. | Hung-Li Chen, Jia-Yang Chen, Yao-Tung Tsou, Chia-Mu Yu, Bo-Chen Tai, Szu-Chuang Li, Yennun Huang, Chia-Ming Lin |