Differential privacy for protecting patient data in speech disorder detection using deep learning

• URL: http://arxiv.org/abs/2409.19078v1
• Date: Fri, 27 Sep 2024 18:25:54 GMT
• Title: Differential privacy for protecting patient data in speech disorder detection using deep learning
• Authors: Soroosh Tayebi Arasteh, Mahshad Lotfinia, Paula Andrea Perez-Toro, Tomas Arias-Vergara, Juan Rafael Orozco-Arroyave, Maria Schuster, Andreas Maier, Seung Hee Yang,
• Abstract summary: This study is the first to investigate differential privacy (DP)'s impact on pathological speech data. We observed a maximum accuracy reduction of 3.85% when training with DP with a privacy budget of 7.51. To generalize our findings, we validated our approach on a smaller dataset of Spanish-speaking Parkinson's disease patients.
• Score: 11.01272267983849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech pathology has impacts on communication abilities and quality of life. While deep learning-based models have shown potential in diagnosing these disorders, the use of sensitive data raises critical privacy concerns. Although differential privacy (DP) has been explored in the medical imaging domain, its application in pathological speech analysis remains largely unexplored despite the equally critical privacy concerns. This study is the first to investigate DP's impact on pathological speech data, focusing on the trade-offs between privacy, diagnostic accuracy, and fairness. Using a large, real-world dataset of 200 hours of recordings from 2,839 German-speaking participants, we observed a maximum accuracy reduction of 3.85% when training with DP with a privacy budget, denoted by {\epsilon}, of 7.51. To generalize our findings, we validated our approach on a smaller dataset of Spanish-speaking Parkinson's disease patients, demonstrating that careful pretraining on large-scale task-specific datasets can maintain or even improve model accuracy under DP constraints. We also conducted a comprehensive fairness analysis, revealing that reasonable privacy levels (2<{\epsilon}<10) do not introduce significant gender bias, though age-related disparities may require further attention. Our results suggest that DP can effectively balance privacy and utility in speech disorder detection, but also highlight the unique challenges in the speech domain, particularly regarding the privacy-fairness trade-off. This provides a foundation for future work to refine DP methodologies and address fairness across diverse patient groups in real-world deployments.

Related papers

• The Impact of Speech Anonymization on Pathology and Its Limits [6.687980678659259]
  This study investigates anonymization's impact on pathological speech across over 2,700 speakers from multiple German institutions. Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate experienced minimal utility changes, while Dysglossia showed slight improvements.
  arXiv  Detail & Related papers  (2024-04-11T18:06:35Z)
• OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition [66.91236298878383]
  Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns. We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
  arXiv  Detail & Related papers  (2024-02-29T01:20:29Z)
• Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection [4.439102809224707]
  Mental health conditions necessitate efficient monitoring to mitigate their adverse impacts on life quality. Existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications. We introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency.
  arXiv  Detail & Related papers  (2024-02-16T18:00:04Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy [5.025818976218807]
  We evaluate the efficacy of DP-enhanced domain transfer (DP-DT) in diagnosing cardiomegaly, pleural effusion, pneumonia, atelectasis, and in identifying healthy subjects. Our results show that DP-DT, even with exceptionally high privacy levels, performs comparably to non-DP-DT.
  arXiv  Detail & Related papers  (2023-06-10T18:41:50Z)
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging [47.99192239793597]
  We evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.
  arXiv  Detail & Related papers  (2023-02-03T09:49:13Z)
• The effect of speech pathology on automatic speaker verification -- a large-scale study [6.468412158245622]
  pathological speech faces heightened privacy breach risks compared to healthy speech. Adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in automatic speaker verification.
  arXiv  Detail & Related papers  (2022-04-13T15:17:00Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.