Scientists are increasingly focusing on developing clinical predictive models that simultaneously guarantee predictive accuracy, interpretability, and patient privacy. José Ramón Pareja Montréal, Juliette Sinnott, and Roger G. Melco, along with colleagues from the Complutense University of Madrid, the University of Waterloo, and the Perimeter Institute for Theoretical Physics, demonstrated that current approaches such as logistic regression and shallow neural networks have significant vulnerabilities to privacy attacks that expose training data. Their work introduces a new quantum-inspired defense that leverages tensor sequences to obfuscate model parameters without sacrificing predictive performance or interpretability. This tensorization technique not only reduces the risk of data leakage by achieving levels comparable to differential privacy, but also increases interpretability by enabling efficient computation of important statistical distributions, establishing a practical path to truly private and effective clinical predictive models.
Tensor train decomposition for privacy-preserving clinical prediction
Researchers have developed a new approach to protect sensitive medical data used in machine learning models while simultaneously increasing interpretability and maintaining predictive accuracy. This study addresses critical challenges in clinical prediction, where models like logistic regression provide transparency but are vulnerable to privacy violations, and more complex neural networks, while potentially more accurate, lack inherent interpretability.
In this work, we introduce a quantum-inspired defense mechanism based on tensorizing a discretized model into a tensor sequence, effectively masking the model parameters without compromising performance. Empirical evaluations demonstrate that this tensorization process significantly reduces the risk of privacy attacks, reduces white-box attacks to random guesses, and achieves black-box protection comparable to differential privacy.
The study begins by highlighting the inherent privacy risks associated with machine learning in clinical settings, where models trained on patient data can inadvertently leak personal information. Our investigation revealed that both logistic regression and shallow neural networks leak important training set information, with logistic regression being particularly susceptible under white box access conditions.
Additionally, standard techniques such as cross-validation unexpectedly exacerbate these vulnerabilities and allow for accurate identification of training data even through public web interfaces. To counter these risks, the team builds on recent advances in tensorization of pre-trained machine learning models and proposes new defenses rooted in tensor network models, specifically tensor trains.
This quantum-inspired technique involves converting a clinical model into a tensor train format, completely obfuscating parameters while preserving accuracy. The researchers applied this method to LORIS, a publicly available logistic regression model for immunotherapy response prediction, and an equivalent neural network model trained for the same task.
Results show that tensorization effectively reduces attack performance across all access levels and provides a practical foundation for private, interpretable, and effective clinical predictions. Importantly, tensor train models not only maintain the interpretability of logistic regression, but also extend it to neural networks and enable efficient computation of marginal and conditional distributions to enhance feature sensitivity analysis.
Membership inference and tensorization for privacy of clinical predictive models
Membership inference attacks supported the assessment of privacy risks associated with clinical prediction models. The researchers designed this attack under both black-box and white-box access conditions to identify the training dataset used to create the model. The methodology involved training multiple shadow models, each with different hyperparameters and datasets, to establish a baseline for comparison.
An adversarial meta-classifier then predicted which public datasets comprised the original model’s training set, effectively revealing training set membership. To evaluate the proposed protection, logistic regression (LR) and shallow neural network (NN) models were trained on the same immunotherapy response prediction task as the publicly available LORIS model.
These models underwent tensorization, a quantum-inspired technique that uses tensor sequences to obfuscate parameters. The discretized output scores were integrated into the tensorization process to further enhance black-box privacy and control the output granularity to tune privacy protection, as well as noise calibration for differential privacy.
We compared the performance with a model protected by differential privacy and evaluated the prediction accuracy in conjunction with privacy leakage. This research leveraged LORIS, which is hosted on a US government website, as the primary test case, enabling attacks via public web interfaces. Importantly, this study demonstrated that tensorization reduced white-box attacks to random guesses, provided black-box protection comparable to differential privacy, and at the same time maintained accuracy levels similar to unprotected models. Additionally, this study reveals that cross-validation, a common technique in LR models like LORIS, can significantly compromise privacy, allowing accurate training set identification even when access is restricted.
Tensor train parameter obfuscation reduces privacy risks in immunotherapy prediction models
Logistic regression (LR) models are found to be particularly vulnerable to privacy attacks in white-box scenarios, leaking critical training set information during empirical evaluation. Our investigation of both LR and shallow neural network (NN) models trained for immunotherapy response prediction reveals that cross-validation practices in LR exacerbate these privacy risks.
To address these vulnerabilities, a quantum-inspired defense was proposed that completely obfuscates the parameters while preserving accuracy by tensorizing the discretized model into a tensor train (TT). White-box attacks were reduced to random guesses by this tensorization, and black-box attacks experienced a degradation comparable to that achieved by differential privacy.
Tensor train models preserve the interpretability of logistic regression and extend it through efficient computation of marginal and conditional distributions, allowing this higher level of interpretability for neural networks as well. Results demonstrate that tensorization is broadly applicable and establishes a practical foundation for private, interpretable, and effective clinical prediction.
In this study, we adopted a shadow model approach with different hyperparameters and datasets to assess privacy risks using membership inference attacks with both black-box and white-box access. Analyzing the publicly available LORIS model (LR Model for Immunotherapy Response Prediction) in parallel with shallow NNs shows that tensorization of the model degrades attack performance across all access levels.
Specifically, white-box attacks were reduced to random guesses, black-box protection was matched with differential privacy protection, and all maintained predictive accuracy close to unprotected models. The output score discretization step size allows you to control privacy protection, similar to using calibrated noise to tune differential privacy.
Additionally, this study demonstrated that using cross-validation for average model deployment significantly compromises privacy, allowing accurate training set identification even from public web interface access. The TT approximation maintains important properties of LORIS such as response monotonicity, increases interpretability through efficient computation of limits and conditionals, supports feature sensitivity analysis, and allows building cancer type-specific models without retraining. The tensorization process is common and can be applied post-training as a practical strategy to create privacy-preserving, interpretable, and effective models in sensitive clinical areas.
Tensor Train decomposition protects privacy and interpretability of clinical machine learning models
Researchers have developed a new defense against privacy vulnerabilities in machine learning models used in clinical settings. This approach is based on tensorizing a discretized model into a tensor sequence, effectively obscuring model parameters while preserving predictive accuracy. Our research revealed that both logistic regression and shallow neural networks leak important training data information, with logistic regression being particularly vulnerable to attack when full access to the model is available.
Additionally, standard techniques such as cross-validation can inadvertently increase these privacy risks. The proposed tensor train technique mitigates these vulnerabilities and reduces the effectiveness of both white-box and black-box attacks to a level comparable to differential privacy. Importantly, this technique preserves the interpretability inherent in logistic regression and extends it to neural networks through efficient computation of statistical distributions.
This provides a deeper understanding of model predictions and facilitates the development of privately interpretable and effective clinical prediction tools. The authors acknowledge that achieving strong privacy guarantees often involves a trade-off between model performance and potential worsening of group disparities.
Future research should focus on improving the application of tensorization to more complex models and datasets. Further research is also needed into the optimal balance between privacy, accuracy, and interpretability. The demonstration of the broad applicability of this tensorization technique establishes a practical foundation for routinely incorporating privacy protection measures into sensitive areas such as clinical prediction.
