Similarity Distribution Based Membership Inference Attack on Person Re-identification

Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are they a natural consequence of imperfect generalization only? Which underlying causes should we address during training to mitigate these attacks? Towards answering such questions, we propose the first approach to explain MI attacks and their connection to generalization based on principled causal reasoning. We offer causal graphs that quantitatively explain the observed MI attack performance achieved for $6$ attack variants. We refute several prior non-quantitative hypotheses that over-simplify or over-estimate the influence of underlying causes, thereby failing to capture the complex interplay between several factors. Our causal models also show a new connection between generalization and MI attacks via their shared causal factors. Our causal models have high predictive power ($0.90$), i.e., their analytical predictions match with observations in unseen experiments often, which makes analysis via them a pragmatic alternative.

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks. When using adversarial defenses to train the robust models, the membership inference advantage increases by up to 4.5 times compared to the naturally undefended models. Beyond revealing the privacy risks of adversarial defenses, we further investigate the factors, such as model capacity, that influence the membership information leakage.

CS-MIA: Membership inference attack based on prediction confidence series in federated learning

With the rapid development of neural network technologies in machine learning, neural networks are widely used in artificial intelligence tasks. Due to the widespread existence of graph data, graph neural networks, a kind of neural network specializing in processing graph data, has become a research hotspot. This paper firstly studies the relationship between adversarial attacks and privacy attacks on graphs, i.e., whether a robust model trained on graph adversarial can improve the attack effect of graph membership inference attacks. We also find the different performance of the robust model’s loss function on the training set and the test set is a critical reason for the increasing membership inference attack success rate. Extensive experimental evaluations on Cora, Cora-ml, Citeseer, Polblogs and Pubmed demonstrate that the robust model obtained by adversarial training can significantly improve the attack success rate of membership inference attacks.KeywordsGraph neural networkMembership inference attackRobust modelAdversarial training

Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indistinguishable in individual shape, statistical distribution and prediction label between members and non-members. The experimental results show that PURIFIER helps defend membership inference attacks with high effectiveness and efficiency, outperforming previous defense methods, and also incurs negligible utility loss. Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks. For example, the inversion error is raised about 4+ times on the Facescrub530 classifier, and the attribute inference accuracy drops significantly when PURIFIER is deployed in our experiment.

Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the privacy concern of membership inference attacks (MIAs). MIAs infer whether an individual's data contributed to the aggregate release. Although effective MIAs have been developed for aggregate location data, these require access to an extensive auxiliary dataset of individual traces over the same locations, which are collected from a similar population. This assumption is often impractical given common privacy practices surrounding location data. To measure the risk of an MIA performed by a realistic adversary, we develop the first Zero Auxiliary Knowledge (ZK) MIA on aggregate location data, which eliminates the need for an auxiliary dataset of real individual traces. Instead, we develop a novel synthetic approach, such that suitable synthetic traces are generated from the released aggregate. We also develop methods to correct for bias and noise, to show that our synthetic-based attack is still applicable when privacy mechanisms are applied prior to release. Using two large-scale location datasets, we demonstrate that our ZK MIA matches the state-of-the-art Knock-Knock (KK) MIA across a wide range of settings, including popular implementations of differential privacy (DP) and suppression of small counts. Furthermore, we show that ZK MIA remains highly effective even when the adversary only knows a small fraction (10%) of their target's location history. This demonstrates that effective MIAs can be performed by realistic adversaries, highlighting the need for strong DP protection.

With the enhanced sensing and computing power of mobile devices, emerging technologies and applications generate massive amounts of data at the edge network, assigning new requirements for improving the privacy and security. Federated learning signifies the advancement in protecting the privacy of intelligent IoT applications. However, recent researches reveal the inherent vulnerabilities of the federated learning that the adversary could obtain the private training data from the publicly shared gradients. Although model distillation is considered a state-of-the-art technique to solve the gradient leakage by hiding gradients, our experiments show that it still has the risk of membership information leakage at the client level. In this paper, we propose a novel client-based membership inference attack in federated distillation learning. Specifically, we first comprehensively analyze model distillation in defensive capabilities for deep gradient leakage. Then, by exploiting that the adversary can learn other participants’ model structure and behavior during federated distillation, we design membership inference attacks against other participants based on private models of malicious clients. Experimental results demonstrate the superiority of our proposed method in terms of efficiency in resolving deep leakage of gradients and high accuracy of membership inference attack in federated distillation learning.KeywordsFederated learningModel distillationMembership inference attack

We consider the problem of membership inference attacks on aggregate survey data through the use of several real-world datasets and a published study as a model for the survey. We apply membership inference attacks from the literature, and discover that methodological assumptions of existing attacks produce a misleading picture of the risk. When using a more realistic methodology, experiments reveal a more nuanced picture of the risk: membership inferences do succeed, but only a small subset of individuals are highly vulnerable to them. In fact, if the adversary wishes to avoid a high false positive rate, she should perform membership inferences only when she has a reason to believe that the target participated in the survey. However, our results do not imply that publishing survey data is inherently safe. Indeed, when applying membership inference not to individuals but to hospitals, we find that highly accurate inferences are possible.

Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. 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. MIAs on ML models can directly lead to a privacy breach. 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 with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models. Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. To further help the researchers, we have created an online resource repository, which we will keep updated with future relevant work. Interested readers can find the repository at https://github.com/HongshengHu/membership-inference-machine-learning-literature.

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.

Deep Learning(DL) techniques have gained significant importance in the recent past due to their vast applications. However, DL is still prone to several attacks, such as the Membership Inference Attack (MIA), based on the memorability of training data. MIA aims at determining the presence of specific data in the training dataset of the model with substitute model of similar structure to the objective model. As MIA relies on the substitute model, they can be mitigated if the substitute model is not clear about the network structure of the objective model. To solve the challenge of shadow-model construction, this work presents L-Leaks, a member inference attack based on Logits. L-Leaks allow an adversary to use the substitute model's information to predict the presence of membership if the shadow and objective model are similar enough. Here, the substitute model is built by learning the logits of the objective model, hence making it similar enough. This results in the substitute model having sufficient confidence in the member samples of the objective model. The evaluation of the attack's success shows that the proposed technique can execute the attack more accurately than existing techniques. It also shows that the proposed MIA is significantly robust under different network models and datasets.

Membership inference attacks can infer whether a data sample exists in the training set of the target model based on limited adversary knowledge, which results in serious leakage of privacy. A large number of recent studies have shown that model overfitting is one of the main reasons why membership inference attacks can be executed successfully. Therefore, some classic methods to solve model overfitting are used to defend against membership inference attacks, such as dropout, spatial dropout, and differential privacy. However, it is difficult for these defense methods to achieve an available trade-off in defense success rate and model utility. In this paper, we focus on the impact of model training loss on model overfitting, and we design a Squeeze-Loss strategy to dynamically find the training loss that achieves the best balance between model utility and privacy. Extensive experimental results show that our strategy can limit the success rate of membership inference attacks to the level of random guesses with almost no loss of model utility, which always outperforms other defense methods.KeywordsMembership inference attackSqueeze training lossDeep learningData privacyDefense strategy

Membership inference attacks (MIAs) are important measures to evaluate potential risks of privacy leakage from machine learning (ML) models. State-of-the-art MIA defenses have achieved favorable privacy-utility trade-offs using knowledge distillation on split training datasets. However, such defenses increase computational costs as a large number of the ML models must be trained on the split datasets. In this study, we proposed a new MIA defense, called SEDMA, based on self-distillation using model aggregation to mitigate the MIAs, inspired by the model parameter averaging as used in federated learning. The key idea of SEDMA is to split the training dataset into several parts and aggregate multiple ML models trained on each split for self-distillation. The intuitive explanation of SEDMA is that model aggregation prevents model over-fitting by smoothing information related to the training data among the multiple ML models and preserving the model utility, such as in federated learning. Through our experiments on major benchmark datasets (Purchase100, Texas100, and CIFAR100), we show that SEDMA outperforms state-of-the-art MIA defenses in terms of membership privacy (MIA accuracy), model accuracy, and computational costs. Specifically, SEDMA incurs at most approximately 3 - 5% model accuracy drop, while achieving the lowest MIA accuracy in state-of-the-art empirical MIA defenses. For computational costs, SEDMA takes significantly less processing time than a defense with the state-of-the-art privacy-utility trade-offs in previous defenses. SEDMA achieves both favorable privacy-utility trade-offs and low computational costs.

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this article, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another relate and similar dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability and introduces slight overhead compared with other defenses. Our experiment also excludes factors that may hinder the performance of DAMIA, and comparisons with other defenses, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.

Conventional membership inference attacks usually require a large number of queries of the target model when training shadow models, and this task becomes extremely difficult when the number of queries is limited. Aiming at the problem of insufficient training data for shadow models due to the limited number of queries, we propose a membership inference attack method based on generative adversarial networks (GAN). First, we use generative adversarial networks to augment the samples obtained by a small number of queries to expand the training data of the model; Secondly, we use the improved CNN to obtain shadow models that have a higher degree of fitting on different target model structures; Finally, we evaluate the accuracy of the proposed algorithm on XgBoost, Logistic, and neural network models using public datasets MNIST and CIFAR10 in a black-box setting, and the results show that our model has an average attack accuracy of 62% and 83%, respectively. It can be seen that, compared with the existing research methods, our model can obtain better attack effects under the condition of significantly reducing the number of queries, which shows the feasibility of our proposed method in membership inference attacks.