Feb 17, 2020 · In this paper, we provide an empirical analysis of the impact of both the data and ML model properties on the vulnerability of ML techniques to ...
Abstract: Machine learning (ML) models have been shown to be vulnerable to Membership Inference At- tacks (MIA), which infer the membership of a given data ...
Mar 24, 2020 · Our analysis indicates a strong relationship between the MIA success and the properties of the data in use, such as the data size and balance ...
Data and model dependencies of membership inference attack. arXiv preprint arXiv:2002.06856. Prerequisites. Run the script.sh file for installing all the ...
Jul 13, 2023 · We evaluate the performance of off-the-shelf MIAs under our generalized membership experiment. We show that dependencies have a strong influence ...
One such attack, the Membership Inference Attack (MIA), exposes whether or not a particular data point was used to train a model. A growing body of literature ...
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model. 1.
[PDF] Investigating Membership Inference Attacks under Data Dependencies
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One such attack, the. Membership Inference Attack (MIA), exposes whether or not a particular data point was used to train a model. A growing body of literature ...
Machine Learning (ML) techniques are used by most data-driven organisations to extract insights. Machine-learning-as-a-service (MLaaS), where models are ...
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What are membership inference attacks?
What are the different types of inference attacks?
Can membership inference be refuted?
What are the characteristics that describe an inference attack?
We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training.
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