Transfer learning is an important approach that produces pre-trained teacher models which can be used to quickly build specialized student models. However, recent research on transfer learning has found that it is vulnerable to various attacks, e.g., misclassification and backdoor attacks. However, it is still not clear whether transfer learning is vulnerable to model inversion attacks. Launching a model inversion attack against transfer learning scheme is challenging. Not only does the student model hide its structural parameters, but it is also not queried to the adversary. Hence, when targeting a student model, existing model inversion attacks fail, as they typically rely on querying the target model. In this paper, we initiate research into model inversion attacks against transfer learning with two novel attack methods. Both are black-box attacks, suiting different situations, that do not rely on queries to the target student model. In the first method, the adversary has the data samples that share the same distribution as the training set of the teacher model. In the second method, the adversary does not have any such samples. Experiments show that highly recognizable data records can be inverted with both of these methods. This research underscores the critical insight that even when a model is shielded from public queries, it can still be susceptible to model inversion attacks.

Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed to counteract these attacks, they often come at the cost of the classifier's utility, thus creating a challenging trade-off between privacy protection and model utility. Moreover, most existing defenses require retraining the classifier for enhanced robustness, which is impractical for large-scale, well-established models. This article introduces a novel defense mechanism to better balance privacy and utility, particularly against adversaries who employ a machine learning model (i.e., inversion model) to reconstruct private data. Drawing inspiration from data poisoning attacks, which can compromise the performance of machine learning models, we propose a strategy that leverages data poisoning to contaminate the training data of inversion models, thereby preventing model inversion attacks. Two defense methods are presented. The first, termed label-preserving poisoning attacks for all output vectors (LPA), involves subtle perturbations to all output vectors while preserving their labels. Our findings demonstrate that these minor perturbations, introduced through a data poisoning approach, significantly increase the difficulty of data reconstruction without compromising the utility of the classifier. Subsequently, we introduce a second method, label-flipping poisoning for partial output vectors (LFP), which selectively perturbs a small subset of output vectors and alters their labels during the process. Empirical results indicate that LPA is notably effective, outperforming the current state-of-the-art defenses. Our data poisoning-based defense provides a new retraining-free defense paradigm that preserves the victim classifier's utility.

Model inversion attacks involve reconstructing the training data of a target model, which raises serious privacy concerns for machine learning models. However, these attacks, especially learning-based methods, are likely to suffer from low attack accuracy, i.e., low classification accuracy of these reconstructed data by machine learning classifiers. Recent studies showed an alternative strategy of model inversion attacks, GAN-based optimization, can improve the attack accuracy effectively. However, these series of GAN-based attacks reconstruct only class-representative training data for a class, whereas learning-based attacks can reconstruct diverse data for different training data in each class. Hence, in this paper, we propose a new training paradigm for a learning-based model inversion attack that can achieve higher attack accuracy in a black-box setting. First, we regularize the training process of the attack model with an added semantic loss function and, second, we inject adversarial examples into the training data to increase the diversity of the class-related parts (i.e., he essential features for classification tasks) in training data. This scheme guides the attack model to pay more attention to the class-related parts of the original data during the data reconstruction process. The experimental results show that our method greatly boosts the performance of existing learning-based model inversion attacks. Even when no extra queries to the target model are allowed, the approach can still improve the attack accuracy of reconstructed data. This new attack shows that the severity of the threat from learning-based model inversion adversaries is underestimated and more robust defenses are required.

Model stealing attacks involve creating copies of machine learning models that have similar functionalities to the original model without proper authorization. Such attacks raise significant concerns about the intellectual property of the machine learning models. Nonetheless, current defense mechanisms against such attacks tend to exhibit certain drawbacks, notably in terms of utility, and robustness. For example, watermarking-based defenses require victim models to be retrained for embedding watermarks, which can potentially impact the main task performance. Moreover, other defenses, especially fingerprinting-based methods, often rely on specific samples like adversarial examples to verify ownership of the target model. These approaches might prove less robust against adaptive attacks, such as model stealing with adversarial training. It remains unclear whether normal examples, as opposed to adversarial ones, can effectively reflect the characteristics of stolen models. To tackle these challenges, we propose a novel method that leverages a neural network as a decoder to inverse the suspicious model’s outputs. Inspired by model inversion attacks, we argue that this decoding process will unveil hidden patterns inherent in the original outputs of the suspicious model. Drawing from these decoding outcomes, we calculate specific metrics to determine the legitimacy of the suspicious models. We validate the efficacy of our defense technique against diverse model stealing attacks, specifically within the domain of classification tasks based on deep neural networks.

In a model inversion attack, an adversary attempts to reconstruct the training data records of a target model using only the model’s output. In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i.e., black-box attacks, or the parameters of a target model, i.e., white-box attacks. However, in the real world, model owners usually only give out the predicted labels; the confidence score vectors and model parameters are hidden as a defense mechanism to prevent such attacks. Unfortunately, we have found a model inversion method that can reconstruct representative samples of the target model’s training data based only on the output labels. We believe this attack requires the least information to succeed and, therefore, has the best applicability. The key idea is to exploit the error rate of the target model to compute the median distance from a set of data records to the decision boundary of the target model. The distance is then used to generate confidence score vectors which are adopted to train an attack model to reconstruct the representative samples. The experimental results show that highly recognizable representative samples can be reconstructed with far less information than existing methods.

Vertical federated learning has a great potential of driving a great variety of business cooperation among enterprises in many fields. In machine learning, decision tree ensembles such as gradient boosting decision trees (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency. However, state-of-art framework for decision tree ensembles in vertical federated learning frameworks adapt anonymous features to avoid possible data breaches, makes the interpretability of the model compromised.To address this issue in the inference process, in this paper, we firstly make a problem analysis about the necessity of disclosure meanings of feature to Guest Party in vertical federated learning. We protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs. The advantages of Fed-EINI will be demonstrated through both theoretical analysis and extensive numerical results. We improve the interpretability of the model by disclosing the meaning of features while ensuring efficiency and accuracy.

We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at O˜e33 .