SoK: Unintended Interactions among Machine Learning Defenses and Risks

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness. Several defenses have been proposed to mitigate such risks. When a defense is effective in mitigating one risk, it may correspond to increased or decreased susceptibility to other risks. Existing research lacks an effective framework to recognize and explain these unintended interactions. We present such a framework, based on the conjecture that overfitting and memorization underlie unintended interactions. We survey existing literature on unintended interactions, accommodating them within our framework. We use our framework to conjecture on two previously unexplored interactions, and empirically validate our conjectures.

Attesting Distributional Properties of Training Data for Machine Learning

The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness. Several jurisdictions are preparing ML regulatory frameworks. One such concern is ensuring that model training data has desirable distributional properties for certain sensitive attributes. For example, draft regulations indicate that model trainers are required to show that training datasets have specific distributional properties, such as reflecting diversity of the population. We propose the notion of property attestation allowing a prover (e.g., model trainer) to demonstrate relevant distributional properties of training data to a verifier (e.g., a customer) without revealing the data. We present an effective hybrid property attestation combining property inference with cryptographic mechanisms.

GrOVe: Ownership Verification of Graph Neural Networks using Embeddings

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the node. Embeddings generated by a GNN for a graph node are unique to that GNN. Prior work has shown that GNNs are prone to model extraction attacks. Model extraction attacks and defenses have been explored extensively in other non-graph settings. While detecting or preventing model extraction appears to be difficult, deterring them via effective ownership verification techniques offer a potential defense. In non-graph settings, fingerprinting models, or the data used to build them, have shown to be a promising approach toward ownership verification. We present GrOVe, a state-of-the-art GNN model fingerprinting scheme that, given a target model and a suspect model, can reliably determine if the suspect model was trained independently of the target model or if it is a surrogate of the target model obtained via model extraction. We show that GrOVe can distinguish between surrogate and independent models even when the independent model uses the same training dataset and architecture as the original target model. Using six benchmark datasets and three model architectures, we show that consistently achieves low false-positive and false-negative rates. We demonstrate that is robust against known fingerprint evasion techniques while remaining computationally efficient.

Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks

Machine learning (ML) models have been deployed for high-stakes applications, e.g., healthcare and criminal justice. Prior work has shown that ML models are vulnerable to attribute inference attacks where an adversary, with some background knowledge, trains an ML attack model to infer sensitive attributes by exploiting distinguishable model predictions. However, some prior attribute inference attacks have strong assumptions about adversary's background knowledge (e.g., marginal distribution of sensitive attribute) and pose no more privacy risk than statistical inference. Moreover, none of the prior attacks account for class imbalance of sensitive attribute in datasets coming from real-world applications (e.g., Race and Sex). In this paper, we propose an practical and effective attribute inference attack that accounts for this imbalance using an adaptive threshold over the attack model's predictions. We exhaustively evaluate our proposed attack on multiple datasets and show that the adaptive threshold over the model's predictions drastically improves the attack accuracy over prior work. Finally, current literature lacks an effective defence against attribute inference attacks. We investigate the impact of fairness constraints (i.e., designed to mitigate unfairness in model predictions) during model training on our attribute inference attack. We show that constraint based fairness algorithms which enforces equalized odds acts as an effective defense against attribute inference attacks without impacting the model utility. Hence, the objective of algorithmic fairness and sensitive attribute privacy are aligned.

Inferring Sensitive Attributes from Model Explanations

Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to its corresponding model prediction. The dependency of explanations on input raises privacy concerns for sensitive user data. However, current literature has limited discussion on privacy risks of model explanations. We focus on the specific privacy risk of attribute inference attack wherein an adversary infers sensitive attributes of an input (e.g., race and sex) given its model explanations. We design the first attribute inference attack against model explanations in two threat models where model builder either (a) includes the sensitive attributes in training data and input or (b) censors the sensitive attributes by not including them in the training data and input. We evaluate our proposed attack on four benchmark datasets and four state-of-the-art algorithms. We show that an adversary can successfully infer the value of sensitive attributes from explanations in both the threat models accurately. Moreover, the attack is successful even by exploiting only the explanations corresponding to sensitive attributes. These suggest that our attack is effective against explanations and poses a practical threat to data privacy. On combining the model predictions (an attack surface exploited by prior attacks) with explanations, we note that the attack success does not improve. Additionally, the attack success on exploiting model explanations is better compared to exploiting only model predictions. These suggest that model explanations are a strong attack surface to exploit for an adversary.

Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks

Machine learning (ML) models have been deployed for high-stakes applications. Due to class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on minority subgroups identified by a sensitive attribute, such as race and sex. In-processing fairness algorithms ensure model predictions are independent of sensitive attribute. Furthermore, ML models are vulnerable to attribute inference attacks where an adversary can identify the values of sensitive attribute by exploiting their distinguishable model predictions. Despite privacy and fairness being important pillars of trustworthy ML, the privacy risk introduced by fairness algorithms with respect to attribute leakage has not been studied. We identify attribute inference attacks as an effective measure for auditing blackbox fairness algorithms to enable model builder to account for privacy and fairness in the model design. We proposed Dikaios, a privacy auditing tool for fairness algorithms for model builders which leveraged a new effective attribute inference attack that account for the class imbalance in sensitive attributes through an adaptive prediction threshold. We evaluated Dikaios to perform a privacy audit of two in-processing fairness algorithms over five datasets. We show that our attribute inference attacks with adaptive prediction threshold significantly outperform prior attacks. We highlighted the limitations of in-processing fairness algorithms to ensure indistinguishable predictions across different values of sensitive attributes. Indeed, the attribute privacy risk of these in-processing fairness schemes is highly variable according to the proportion of the sensitive attributes in the dataset. This unpredictable effect of fairness mechanisms on the attribute privacy risk is an important limitation on their utilization which has to be accounted by the model builder.

SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning

Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an ML model, risk violating membership privacy. ML model builders need a principled definition of a metric that enables them to quantify the privacy risk of (a) individual training data records, (b) independently of specific MIAs, (c) efficiently. None of the prior work on membership privacy risk metrics simultaneously meets all of these criteria. We propose such a metric, SHAPr, which uses Shapley values to quantify a model's memorization of an individual training data record by measuring its influence on the model's utility. This memorization is a measure of the likelihood of a successful MIA. Using ten benchmark datasets, we show that SHAPr is effective (precision: 0.94$\pm 0.06$, recall: 0.88$\pm 0.06$) in estimating susceptibility of a training data record for MIAs, and is efficient (computable within minutes for smaller datasets and in ~90 minutes for the largest dataset). SHAPr is also versatile in that it can be used for other purposes like assessing fairness or assigning valuation for subsets of a dataset. For example, we show that SHAPr correctly captures the disproportionate vulnerability of different subgroups to MIAs. Using SHAPr, we show that the membership privacy risk of a dataset is not necessarily improved by removing high risk training data records, thereby confirming an observation from prior work in a significantly extended setting (in ten datasets, removing up to 50% of data).

Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks

Machine learning models are typically made available to potential client users via inference APIs. Model extraction attacks occur when a malicious client uses information gleaned from queries to the inference API of a victim model $F_V$ to build a surrogate model $F_A$ that has comparable functionality. Recent research has shown successful model extraction attacks against image classification, and NLP models. In this paper, we show the first model extraction attack against real-world generative adversarial network (GAN) image translation models. We present a framework for conducting model extraction attacks against image translation models, and show that the adversary can successfully extract functional surrogate models. The adversary is not required to know $F_V$'s architecture or any other information about it beyond its intended image translation task, and queries $F_V$'s inference interface using data drawn from the same domain as the training data for $F_V$. We evaluate the effectiveness of our attacks using three different instances of two popular categories of image translation: (1) Selfie-to-Anime and (2) Monet-to-Photo (image style transfer), and (3) Super-Resolution (super resolution). Using standard performance metrics for GANs, we show that our attacks are effective in each of the three cases -- the differences between $F_V$ and $F_A$, compared to the target are in the following ranges: Selfie-to-Anime: FID $13.36-68.66$, Monet-to-Photo: FID $3.57-4.40$, and Super-Resolution: SSIM: $0.06-0.08$ and PSNR: $1.43-4.46$. Furthermore, we conducted a large scale (125 participants) user study on Selfie-to-Anime and Monet-to-Photo to show that human perception of the images produced by the victim and surrogate models can be considered equivalent, within an equivalence bound of Cohen's $d=0.3$.

GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning

Embedded systems demand on-device processing of data using Neural Networks (NNs) while conforming to the memory, power and computation constraints, leading to an efficiency and accuracy tradeoff. To bring NNs to edge devices, several optimizations such as model compression through pruning, quantization, and off-the-shelf architectures with efficient design have been extensively adopted. These algorithms when deployed to real world sensitive applications, requires to resist inference attacks to protect privacy of users training data. However, resistance against inference attacks is not accounted for designing NN models for IoT. In this work, we analyse the three-dimensional privacy-accuracy-efficiency tradeoff in NNs for IoT devices and propose Gecko training methodology where we explicitly add resistance to private inferences as a design objective. We optimize the inference-time memory, computation, and power constraints of embedded devices as a criterion for designing NN architecture while also preserving privacy. We choose quantization as design choice for highly efficient and private models. This choice is driven by the observation that compressed models leak more information compared to baseline models while off-the-shelf efficient architectures indicate poor efficiency and privacy tradeoff. We show that models trained using Gecko methodology are comparable to prior defences against black-box membership attacks in terms of accuracy and privacy while providing efficiency.

Quantifying (Hyper) Parameter Leakage in Machine Learning

Black Box Machine Learning models leak information about the proprietary model parameters and architecture, both through side channels and output predictions. An adversary can thus, exploit this leakage to reconstruct a substitute architecture similar to the target model, violating the model privacy and Intellectual Property. However, all such attacks, infer a subset of the target model attributes and identifying the rest of the architecture and parameters (optimally) is a search problem. Extracting the exact target model is not possible owing to the uncertainty in the inference attack outputs and stochastic nature of the training process. In this work, we propose a probabilistic framework, Airavata, to estimate the leakage in such model extraction attacks. Specifically, we use Bayesian Networks to capture the uncertainty, under the subjective notion of probability, in estimating the target model attributes using various model extraction attacks. We experimentally validate the model under different adversary assumptions commonly adopted by various model extraction attacks to reason about the attack efficacy. Further, this provides a practical approach of inferring actionable knowledge about extracting black box models and identify the best combination of attacks which maximise the knowledge extracted (information leaked) from the target model.