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Membership Inference Attack Susceptibility of Clinical Language Models
[article]
2021
arXiv
pre-print
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We show that membership inference attacks on CLMs lead to non-trivial privacy leakages of up to 7%. ...
We design and employ membership inference attacks to estimate the empirical privacy leaks for model architectures like BERT and GPT2. ...
Samples For membership inference experiments in CLMs, the sample S consists of the input sentence and the constructed language modeling target. ...
arXiv:2104.08305v1
fatcat:a3n3hgwi6fd2jovuapcdha3gey
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
[article]
2022
arXiv
pre-print
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We show that masked language models are extremely susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves ...
Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying the potential robustness of MLMs to privacy attacks. ...
We evaluate our proposed attack on a suite of masked clinical language models, following (Lehman et al., 2021) . ...
arXiv:2203.03929v1
fatcat:4l2lyxt3bjes7ckh5c5rsevm6a
Are Clinical BERT Models Privacy Preserving? The Difficulty of Extracting Patient-Condition Associations
2021
AAAI Fall Symposia
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This article explores whether BERT models trained on clinical data are susceptible to training data extraction attacks. ...
Language models may be trained on data that contain personal information, such as clinical data. Such sensitive data must not leak for privacy reasons. ...
Membership Inference Attacks
Unmasking Pseudonymized Training Data If a language model M has been trained on a dataset D, then there is a risk that the model has memorized certain sensitive details. ...
dblp:conf/aaaifs/VakiliD21
fatcat:zxdosrh6i5gkbn4ysrolorph24
Making machine learning trustworthy
2021
Science
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x
Adversary
Output y
Model stealing
Model evasion
Membership
inference ...
Research has made progress on detecting poisoning and adversarial inputs to limiting what an adversary may learn by just interacting with a model to limit the extent of model stealing or membership inference ...
doi:10.1126/science.abi5052
fatcat:qjnee5ile5ftbbdgwvkh65dima
Membership Inference Attacks on Machine Learning: A Survey
[article]
2022
arXiv
pre-print
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For example, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease ...
However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. ...
Unlike model extraction attacks targeting the ML model, the attacker of an attribute inference attack, property inference attack or membership inference attack focuses on inferring private information ...
arXiv:2103.07853v4
fatcat:fwdoonsgnfbhncxkgmra2xk32y
Towards Demystifying Membership Inference Attacks
[article]
2019
arXiv
pre-print
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First, we provide a generalized formulation of the development of a black-box membership inference attack model. ...
Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF and IISP ...
arXiv:1807.09173v2
fatcat:5vmtqv5glndphomymzpm2k2rpu
When Machine Unlearning Jeopardizes Privacy
[article]
2020
arXiv
pre-print
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We propose a novel membership inference attack which leverages the different outputs of an ML model's two versions to infer whether the deleted sample is part of the training set. ...
More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive ...
Previous studies [54, 63] have shown that overfitted models are more susceptible to classical membership inference attacks, while well-generalized models are almost immune to them. ...
arXiv:2005.02205v1
fatcat:nejb2afuqfe4pcrv463zgfjbna
Survey: Leakage and Privacy at Inference Time
[article]
2021
arXiv
pre-print
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We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. ...
We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks ...
Membership Inference Attacks ML models currently do not fall under GDPR protection. ...
arXiv:2107.01614v1
fatcat:76a724yzkjfvjisrokssl6assa
LOGAN: Membership Inference Attacks Against Generative Models
[article]
2018
arXiv
pre-print
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In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. ...
We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects ...
Membership Inference Attacks Against Generative Models In this section, we present our membership inference attacks against generative models. ...
arXiv:1705.07663v4
fatcat:amddvcw7i5gf5jeh6wzisgavd4
LOGAN: Membership Inference Attacks Against Generative Models
2019
Proceedings on Privacy Enhancing Technologies
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In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. ...
We present attacks based on both white-box and black-box access to the target model, against several state-of-the-art generative models, over datasets of complex representations of faces (LFW), objects ...
We present the first study of membership inference attacks on generative models; 2. ...
doi:10.2478/popets-2019-0008
dblp:journals/popets/HayesMDC19
fatcat:2x3xnelx3zf5tgkkufvgvubzzq
Smallest of Maximum to find α-predicate for DeterminingCattle Health Conditions
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Then α-predicate is reprocessed by defuzzification of the Tsukamoto model and produces handling suggestions and information on the cattle's condition. ...
This study tries to apply the fuzzy algorithm into an animal inspection expert system at the Cimanggu Animal Clinic. ...
Inference System Fuzzy inference systems are also known as fuzzy rulebased systems, fuzzy associative memory, fuzzy models, or fuzzy controllers [19] . ...
doi:10.30534/ijatcse/2020/192952020
fatcat:pi3bz3eptzblti4olfhacpnavy
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1905.07002v1 fatcat:eqmisiq7qzhmtgnww25wepfwbm
Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements. ...
De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. ...
One example to such well crafted attacks are the membership inference attacks proposed by Shokri et al. (2017) . ...
arXiv:1905.07002v2
fatcat:m4txx3e6uzalhpvtjzjepojthi
Active Data Pattern Extraction Attacks on Generative Language Models
[article]
2022
arXiv
pre-print
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With the wide availability of large pre-trained language model checkpoints, such as GPT-2 and BERT, the recent trend has been to fine-tune them on a downstream task to achieve the state-of-the-art performance ...
We further analyse the privacy impact of specific components, e.g. the decoding strategy, pertained to this application through our attack settings. ...
In Proceedings of the 2016 ACM Con- ...
arXiv:2207.10802v1
fatcat:eq4wfmrpbjeshnjxqedpzujpom
Measuring Utility and Privacy of Synthetic Genomic Data
[article]
2021
arXiv
pre-print
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We then measure privacy through the lens of membership inference attacks, i.e., inferring whether a record was part of the training data. ...
Moreover, while some combinations of datasets and models produce synthetic data with distributions close to the real data, there often are target data points that are vulnerable to membership inference ...
Google Faculty Award on "Enabling Progress in Genomic Research via Privacy Preserving Data Sharing" and a grant from the NCSC and the Alan Turing Institute on "Evaluating Privacy-Preserving Generative Models ...
arXiv:2102.03314v2
fatcat:rho3sjwbjfeezd6e4avusn6byu
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
[article]
2022
arXiv
pre-print
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However, recent works have demonstrated that FL approaches are still vulnerable to various privacy attacks like reconstruction attacks and membership inference attacks. ...
To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or ...
of privacy attacks, including membership inference attacks [12] and reconstruction attacks [13] , [14] . ...
arXiv:2112.13416v2
fatcat:nrw7l7r3p5ff3ac2n6akimqqoi
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