Details

Autor(en) / Beteiligte

• Gwon, Hansle
• Ahn, Imjin
• Kim, Yunha
• Kang, Hee Jun
• Seo, Hyeram
• Choi, Heejung
• Cho, Ha Na
• Kim, Minkyoung
• Han, JiYe
• Kee, Gaeun
• Park, Seohyun
• Lee, Kye Hwa
• Jun, Tae Joon
• Kim, Young-Hak

Titel

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis

Ist Teil von

• Computers in biology and medicine, 2024-01, Vol.168, p.107738-107738, Article 107738

Ort / Verlag

United States: Elsevier Limited

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.