How Does Data Augmentation Affect Privacy in Machine Learning?
Authors: Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu | Published: 2020-07-21 | Updated: 2021-02-26
プライバシー評価
Differentially Private Simple Linear Regression
Authors: Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam Smith, Salil Vadhan | Published: 2020-07-10
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
Authors: Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal | Published: 2020-07-08
Sharing Models or Coresets: A Study based on Membership Inference Attack
Authors: Hanlin Lu, Changchang Liu, Ting He, Shiqiang Wang, Kevin S. Chan | Published: 2020-07-06
PPaaS: Privacy Preservation as a Service
Authors: Pathum Chamikara Mahawaga Arachchige, Peter Bertok, Ibrahim Khalil, Dongxi Liu, Seyit Camtepe | Published: 2020-07-04 | Updated: 2021-04-21
On the Effectiveness of Regularization Against Membership Inference Attacks
Authors: Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras | Published: 2020-06-09
Secure Byzantine-Robust Machine Learning
Authors: Lie He, Sai Praneeth Karimireddy, Martin Jaggi | Published: 2020-06-08 | Updated: 2020-10-18
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
Authors: Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis Bach | Published: 2020-06-08 | Updated: 2021-10-28
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Authors: Qian Lou, Song Bian, Lei Jiang | Published: 2020-06-07 | Updated: 2020-10-15
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Authors: Hanieh Hashemi, Yongqin Wang, Murali Annavaram | Published: 2020-06-01 | Updated: 2020-10-15