How to combine membership-inference attacks on multiple updated models
A large body of research has shown that machine learning models are vulnerable to
membership inference (MI) attacks that violate the privacy of the participants in the training …
membership inference (MI) attacks that violate the privacy of the participants in the training …
How to combine membership-inference attacks on multiple updated machine learning models
A large body of research has shown that machine learning models are vulnerable to
membership inference (MI) attacks that violate the privacy of the participants in the training …
membership inference (MI) attacks that violate the privacy of the participants in the training …
Overconfidence is a dangerous thing: Mitigating membership inference attacks by enforcing less confident prediction
Z Chen, K Pattabiraman - arXiv preprint arXiv:2307.01610, 2023 - arxiv.org
Machine learning (ML) models are vulnerable to membership inference attacks (MIAs),
which determine whether a given input is used for training the target model. While there …
which determine whether a given input is used for training the target model. While there …
Membership inference attacks: analysis and mitigation
MSR Shuvo, D Alhadidi - … on Trust, Security and Privacy in …, 2020 - ieeexplore.ieee.org
Given a machine learning model and a record, membership attacks determine whether this
record was used as part of the model's training dataset. Membership inference can present a …
record was used as part of the model's training dataset. Membership inference can present a …
Mitigating Membership Inference Attacks via Weighted Smoothing
Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to
membership inference attack (MIA), where adversaries can determine if specific data was …
membership inference attack (MIA), where adversaries can determine if specific data was …
Dissecting membership inference risk in machine learning
N Senavirathne, V Torra - … 13th International Symposium, CSS 2021, Virtual …, 2022 - Springer
Membership inference attacks (MIA) have been identified as a distinct threat to privacy when
sensitive personal data are used to train the machine learning (ML) models. This work is …
sensitive personal data are used to train the machine learning (ML) models. This work is …
[HTML][HTML] A survey on membership inference attacks and defenses in Machine Learning
Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
A pragmatic approach to membership inferences on machine learning models
Membership Inference Attacks (MIAs) aim to determine the presence of a record in a
machine learning model's training data by querying the model. Recent work has …
machine learning model's training data by querying the model. Recent work has …
Mitigating membership inference attacks by {Self-Distillation} through a novel ensemble architecture
Membership inference attacks are a key measure to evaluate privacy leakage in machine
learning (ML) models. It is important to train ML models that have high membership privacy …
learning (ML) models. It is important to train ML models that have high membership privacy …
Defenses to membership inference attacks: A survey
L Hu, A Yan, H Yan, J Li, T Huang, Y Zhang… - ACM Computing …, 2023 - dl.acm.org
Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …
computer vision and natural language processing. However, ML models are vulnerable to …