身份验证系统容易受到模型反演攻击的影响，在这种攻击中，对手能够近似目标机器学习模型的倒数。生物识别模型是这种攻击的主要候选者。这是因为反相生物特征模型允许攻击者产生逼真的生物识别输入，以使生物识别认证系统欺骗。进行成功模型反转攻击的主要限制之一是所需的训练数据量。在这项工作中，我们专注于虹膜和面部生物识别系统，并提出了一种新技术，可大大减少必要的训练数据量。通过利用多个模型的输出，我们能够使用1/10进行模型反演攻击，以艾哈迈德和富勒（IJCB 2020）的训练集大小（IJCB 2020）进行虹膜数据，而Mai等人的训练集大小为1/1000。 （模式分析和机器智能2019）的面部数据。我们将新的攻击技术表示为结构性随机，并损失对齐。我们的攻击是黑框，不需要了解目标神经网络的权重，只需要输出向量的维度和值。为了显示对齐损失的多功能性，我们将攻击框架应用于会员推理的任务（Shokri等，IEEE S＆P 2017），对生物识别数据。对于IRIS，针对分类网络的会员推断攻击从52％提高到62％的准确性。

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. 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 dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on.We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

机器学习（ML）模型已广泛应用于各种应用，包括图像分类，文本生成，音频识别和图形数据分析。然而，最近的研究表明，ML模型容易受到隶属推导攻击（MIS），其目的是推断数据记录是否用于训练目标模型。 ML模型上的MIA可以直接导致隐私违规行为。例如，通过确定已经用于训练与某种疾病相关的模型的临床记录，攻击者可以推断临床记录的所有者具有很大的机会。近年来，MIS已被证明对各种ML模型有效，例如，分类模型和生成模型。同时，已经提出了许多防御方法来减轻米西亚。虽然ML模型上的MIAS形成了一个新的新兴和快速增长的研究区，但还没有对这一主题进行系统的调查。在本文中，我们对会员推论和防御进行了第一个全面调查。我们根据其特征提供攻击和防御的分类管理，并讨论其优点和缺点。根据本次调查中确定的限制和差距，我们指出了几个未来的未来研究方向，以激发希望遵循该地区的研究人员。这项调查不仅是研究社区的参考，而且还为该研究领域之外的研究人员带来了清晰的照片。为了进一步促进研究人员，我们创建了一个在线资源存储库，并与未来的相关作品继续更新。感兴趣的读者可以在https://github.com/hongshenghu/membership-inference-machine-learning-literature找到存储库。

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.

模型反演（MI），其中对手滥用访问训练有素的机器学习（ML）模型试图推断有关其原始培训数据的敏感信息，引起了越来越多的研究关注。在MI期间，攻击（MUA）的训练模型通常被冻结并用于指导发电机的训练，例如生成的对抗网络（GAN），以重建该模型的原始训练数据的分布。这可能导致原始培训样本泄漏，如果成功，如果培训数据包含个人身份信息（PII），则数据集科目的隐私将面临风险。因此，对MI技术潜力的深入研究对于开发相应的防御技术至关重要。基于单一模型的培训数据的高质量重建是具有挑战性的。然而，现有的MI文献不会共同探索定位多个模型，这可以为对手提供额外的信息和多样化的观点。我们提出了集合反转技术，其通过培训由具有共享主题或实体的培训模型的集合（或设置）的生成器来培训产生的发电机的分布。与单个ML模型的MI相比，该技术导致产生具有数据集实体的具有可区分特征的所产生的样本的质量的显着改善。我们在没有任何数据集的情况下实现了高质量的结果，并显示了如何利用类似于假定训练数据的辅助数据集来提高结果。模型多样性在集合中的影响是彻底调查的，并且利用了额外的约束来鼓励重建样本的尖锐预测和高激活，从而导致训练图像的更准确地重建。

虽然机器学习（ML）在过去十年中取得了巨大进展，但最近的研究表明，ML模型易受各种安全和隐私攻击的影响。到目前为止，这场领域的大部分攻击都专注于由分类器代表的歧视模型。同时，一点关注的是生成模型的安全性和隐私风险，例如生成的对抗性网络（GANS）。在本文中，我们提出了对GANS的第一组培训数据集财产推论攻击。具体地，对手旨在推断宏观级训练数据集属性，即用于训练目标GaN的样本的比例，用于某个属性。成功的财产推理攻击可以允许对手来获得目标GaN的训练数据集的额外知识，从而直接违反目标模型所有者的知识产权。此外，它可以用作公平审计员，以检查目标GAN是否接受偏置数据集进行培训。此外，财产推理可以用作其他高级攻击的构建块，例如隶属推断。我们提出了一般的攻击管道，可以根据两个攻击场景量身定制，包括全黑盒设置和部分黑盒设置。对于后者，我们介绍了一种新颖的优化框架来增加攻击效果。在五个房产推理任务上超过四个代表性GaN模型的广泛实验表明我们的攻击实现了强大的表现。此外，我们表明我们的攻击可用于增强隶属推断对GANS的绩效。

Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications.However, the early demonstrations of the feasibility of such attacks have many assumptions on the adversary, such as using multiple so-called shadow models, knowledge of the target model structure, and having a dataset from the same distribution as the target model's training data. We relax all these key assumptions, thereby showing that such attacks are very broadly applicable at low cost and thereby pose a more severe risk than previously thought. We present the most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains.In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.

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 stateof-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.

模型反转攻击（MIAS）旨在创建合成图像，通过利用模型的学习知识来反映目标分类器的私人培训数据中的班级特征。先前的研究开发了生成的MIA，该MIA使用生成的对抗网络（GAN）作为针对特定目标模型的图像先验。这使得攻击时间和资源消耗，不灵活，并且容易受到数据集之间的分配变化的影响。为了克服这些缺点，我们提出了插头攻击，从而放宽了目标模型和图像之前的依赖性，并启用单个GAN来攻击广泛的目标，仅需要对攻击进行少量调整。此外，我们表明，即使在公开获得的预训练的gan和强烈的分配转变下，也可以实现强大的MIA，而先前的方法无法产生有意义的结果。我们的广泛评估证实了插头攻击的鲁棒性和灵活性，以及​​它们创建高质量图像的能力，揭示了敏感的类特征。

Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., using it to determine the auxiliary data for model-inversion attacks. However, attackers may not know what the model is used for in practice. We propose a generative adversarial network (GAN) based method to explore likely or similar domains of a target model -- the model domain inference (MDI) attack. For a given target (classification) model, we assume that the attacker knows nothing but the input and output formats and can use the model to derive the prediction for any input in the desired form. Our basic idea is to use the target model to affect a GAN training process for a candidate domain's dataset that is easy to obtain. We find that the target model may distract the training procedure less if the domain is more similar to the target domain. We then measure the distraction level with the distance between GAN-generated datasets, which can be used to rank candidate domains for the target model. Our experiments show that the auxiliary dataset from an MDI top-ranked domain can effectively boost the result of model-inversion attacks.

窃取对受控信息的攻击，以及越来越多的信息泄漏事件，已成为近年来新兴网络安全威胁。由于蓬勃发展和部署先进的分析解决方案，新颖的窃取攻击利用机器学习（ML）算法来实现高成功率并导致大量损坏。检测和捍卫这种攻击是挑战性和紧迫的，因此政府，组织和个人应该非常重视基于ML的窃取攻击。本调查显示了这种新型攻击和相应对策的最新进展。以三类目标受控信息的视角审查了基于ML的窃取攻击，包括受控用户活动，受控ML模型相关信息和受控认证信息。最近的出版物总结了概括了总体攻击方法，并导出了基于ML的窃取攻击的限制和未来方向。此外，提出了从三个方面制定有效保护的对策 - 检测，破坏和隔离。

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data.Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15.Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

机器学习模型容易记住敏感数据，使它们容易受到会员推理攻击的攻击，其中对手的目的是推断是否使用输入样本来训练模型。在过去的几年中，研究人员产生了许多会员推理攻击和防御。但是，这些攻击和防御采用各种策略，并在不同的模型和数据集中进行。但是，缺乏全面的基准意味着我们不了解现有攻击和防御的优势和劣势。我们通过对不同的会员推理攻击和防御措施进行大规模测量来填补这一空白。我们通过研究九项攻击和六项防御措施来系统化成员的推断，并在整体评估中衡量不同攻击和防御的性能。然后，我们量化威胁模型对这些攻击结果的影响。我们发现，威胁模型的某些假设，例如相同架构和阴影和目标模型之间的相同分布是不必要的。我们也是第一个对从Internet收集的现实世界数据而不是实验室数据集进行攻击的人。我们进一步研究是什么决定了会员推理攻击的表现，并揭示了通常认为过度拟合水平不足以成功攻击。取而代之的是，成员和非成员样本之间的熵/横向熵的詹森 - 香农距离与攻击性能的相关性更好。这为我们提供了一种新的方法，可以在不进行攻击的情况下准确预测会员推理风险。最后，我们发现数据增强在更大程度上降低了现有攻击的性能，我们提出了使用增强作用的自适应攻击来训练阴影和攻击模型，以改善攻击性能。

员额推理攻击允许对训练的机器学习模型进行对手以预测模型的训练数据集中包含特定示例。目前使用平均案例的“精度”度量来评估这些攻击，该攻击未能表征攻击是否可以自信地识别培训集的任何成员。我们认为，应该通过计算其低（例如<0.1％）假阳性率来计算攻击来评估攻击，并在以这种方式评估时发现大多数事先攻击差。为了解决这一问题，我们开发了一个仔细结合文献中多种想法的似然比攻击（Lira）。我们的攻击是低于虚假阳性率的10倍，并且在攻击现有度量的情况下也严格占主导地位。

Collaborative machine learning and related techniques such as federated learning allow multiple participants, each with his own training dataset, to build a joint model by training locally and periodically exchanging model updates. We demonstrate that these updates leak unintended information about participants' training data and develop passive and active inference attacks to exploit this leakage. First, we show that an adversarial participant can infer the presence of exact data points-for example, specific locations-in others' training data (i.e., membership inference). Then, we show how this adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. For example, he can infer when a specific person first appears in the photos used to train a binary gender classifier. We evaluate our attacks on a variety of tasks, datasets, and learning configurations, analyze their limitations, and discuss possible defenses.

从公共机器学习（ML）模型中泄漏数据是一个越来越重要的领域，因为ML的商业和政府应用可以利用多个数据源，可能包括用户和客户的敏感数据。我们对几个方面的当代进步进行了全面的调查，涵盖了非自愿数据泄漏，这对ML模型很自然，潜在的恶毒泄漏是由隐私攻击引起的，以及目前可用的防御机制。我们专注于推理时间泄漏，这是公开可用模型的最可能场景。我们首先在不同的数据，任务和模型体系结构的背景下讨论什么是泄漏。然后，我们提出了跨非自愿和恶意泄漏的分类法，可用的防御措施，然后进行当前可用的评估指标和应用。我们以杰出的挑战和开放性的问题结束，概述了一些有希望的未来研究方向。

如今，深度学习模型的所有者和开发人员必须考虑其培训数据的严格隐私保护规则，通常是人群来源且保留敏感信息。如今，深入学习模型执行隐私保证的最广泛采用的方法依赖于实施差异隐私的优化技术。根据文献，这种方法已被证明是针对多种模型的隐私攻击的成功防御，但其缺点是对模型的性能的实质性降级。在这项工作中，我们比较了差异私有的随机梯度下降（DP-SGD）算法与使用正则化技术的标准优化实践的有效性。我们分析了生成模型的实用程序，培训性能以及成员推理和模型反转攻击对学习模型的有效性。最后，我们讨论了差异隐私的缺陷和限制，并从经验上证明了辍学和L2型规范的卓越保护特性。

会员推理攻击是机器学习模型中最简单的隐私泄漏形式之一：给定数据点和模型，确定该点是否用于培训模型。当查询其培训数据时，现有会员推理攻击利用模型的异常置信度。如果对手访问模型的预测标签，则不会申请这些攻击，而不会置信度。在本文中，我们介绍了仅限标签的会员资格推理攻击。我们的攻击而不是依赖置信分数，而是评估模型预测标签在扰动下的稳健性，以获得细粒度的隶属信号。这些扰动包括常见的数据增强或对抗例。我们经验表明，我们的标签占会员推理攻击与先前攻击相符，以便需要访问模型信心。我们进一步证明，仅限标签攻击违反了（隐含或明确）依赖于我们呼叫信心屏蔽的现象的员工推论攻击的多种防御。这些防御修改了模型的置信度分数以挫败攻击，但留下模型的预测标签不变。我们的标签攻击展示了置信性掩蔽不是抵御会员推理的可行的防御策略。最后，我们调查唯一的案例标签攻击，该攻击推断为少量异常值数据点。我们显示仅标签攻击也匹配此设置中基于置信的攻击。我们发现具有差异隐私和（强）L2正则化的培训模型是唯一已知的防御策略，成功地防止所有攻击。即使差异隐私预算太高而无法提供有意义的可证明担保，这仍然存在。

机器学习（ML）已将自身驾驶到认证系统的自主驱动范围的各种关键应用的基石。然而，随着机器学习模型的增加，已经出现了多次攻击。一类这样的攻击正在培训时间攻击，由此对手在机器学习模型培训之前或期间执行他们的攻击。在这项工作中，我们提出了一种对基于计算机视觉的机器学习模型的新培训时间攻击，即模型劫持攻击。对手旨在劫持目标模型，而不是模特所有者注意到的原始任务。模型劫持可能会导致问责制和安全风险，因为可以将劫持型号所有者构成，以便拥有其型号提供非法或不道德的服务。模型劫持攻击以与现有数据中毒攻击相同的方式启动。然而，模型劫持攻击的一个要求是隐身，即劫持目标模型的数据样本应该类似于模型的原始训练数据集。为此，我们提出了两种不同的模型劫持攻击，即Chameleon和不良变色龙，基于新颖的编码器解码器样式ML模型，即Camouflager。我们的评价表明，我们的模型劫持攻击都达到了高攻击成功率，模型实用程序下降了不计。

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis.The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., "steal") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.