Systematic evaluation of privacy risks of machine learning models
Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …
to membership inference attacks in which an adversary aims to guess if an input sample was …
[PDF][PDF] Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - swh.princeton.edu
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
Systematic evaluation of privacy risks of machine learning models
L Song, P Mittal - 30th USENIX Security Symposium …, 2021 - collaborate.princeton.edu
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - arXiv preprint arXiv:2003.10595, 2020 - arxiv.org
Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …
to membership inference attacks in which an adversary aims to guess if an input sample was …
Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
[PDF][PDF] Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song - pdfs.semanticscholar.org
❑ Apply all benchmark attack methods and report the highest attack accuracy❑ Both
adversarial regularization (AdvReg, CCS'18) and MemGuard (CCS'19) reported to decrease …
adversarial regularization (AdvReg, CCS'18) and MemGuard (CCS'19) reported to decrease …
[PDF][PDF] Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - princeton.edu
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
[PDF][PDF] Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - usenix.org
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
[PDF][PDF] Systematic Evaluation of Privacy Risks of Machine Learning Models
L Song, P Mittal - princeton.edu
Abstract Machine learning models are prone to memorizing sensitive data, making them
vulnerable to membership inference attacks in which an adversary aims to guess if an input …
vulnerable to membership inference attacks in which an adversary aims to guess if an input …