CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning
Since machine learning model is often trained on a limited data set, the model is trained multiple times on the same data sample, which causes the model to memorize most of the training set data. Membership Inference Attacks (MIAs) exploit this feature to determine whether a data sample is used for training a machine learning model. However, in realistic scenarios, it is difficult for the adversary to obtain enough qualified samples that mark accurate identity information, especially since most samples are non-members in real world applications. To address this limitation, in this paper, we propose a new attack method called CLMIA, which uses unsupervised contrastive learning to train an attack model without using extra membership status information. Meanwhile, in CLMIA, we require only a small amount of data with known membership status to fine-tune the attack model. Experimental results demonstrate that CLMIA performs better than existing attack methods for different datasets and model structures, especially with data with less marked identity information. In addition, we experimentally find that the attack performs differently for different proportions of labeled identity information for member and non-member data. More analysis proves that our attack method performs better with less labeled identity information, which applies to more realistic scenarios.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Quantifying and Mitigating Privacy Risks of Contrastive Learning
Data is the key factor to drive the development of machine learning (ML) during the past decade. However, high-quality data, in particular labeled data, is often hard and expensive to collect. To leverage large-scale unl…
AttributeBIG-bench Machine LearningContrastive LearningData Augmentation+2DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LLMs. Automating document processing workf…
document understandingInference AttackMembership Inference AttackQuestion Answering+1Label-Only Membership Inference Attacks
Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to train the model. Existing membership infere…
L2 RegularizationEnsembling Membership Inference Attacks Against Tabular Generative Models
Membership Inference Attacks (MIAs) have emerged as a principled framework for auditing the privacy of synthetic data generated by tabular generative models, where many diverse methods have been proposed that each exploi…
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, privacy researchers have demonstrated that th…
AttributeBIG-bench Machine LearningInference Attack