Differential privacy protection over deep learning: An investigation of its impacted factors

• Main Authors: LIN, Ying, BAO, Ling-Yan, LI, Ze-Minghui, SI, Shu-Sheng, CHU, Chao-Hsien
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Differential privacy; Privacy preserving; Deep learning; Stochastic gradient descent (SGD); Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5402; https://ink.library.smu.edu.sg/context/sis_research/article/6405/viewcontent/DifferentialPrivacy_av_2020.pdf
• Institution: Singapore Management University

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the...