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19th International Workshop on data privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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privacy preserving data Imputation via Multi-party Computation for medical Applications

Privacy Preserving Data Imputation via Multi-party Computati...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Jentsch, Julia Ünal, Ali Burak Mağara, Şeyma Selcan Akgün, Mete Department of Computer Science Medical Data Privacy and Privacy Preserving Machine Learning Tübingen Germany

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350548

Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whole dataset, which contradicts the privacy regulations when the data is distributed among multiple sources. Especially in the medical and healthcare domain, such access reveals sensitive information about patients. This study addresses privacy-preserving imputation methods for sensitive data using secure multi-party computation, enabling secure computations without revealing any party’s sensitive information. In this study, we realized the mean, median, regression, and kNN imputation methods in a privacy-preserving way. We specifically target the medical and healthcare domains considering the significance of protection of the patient data, showcasing our methods on a diabetes dataset. Experiments on the diabetes dataset validated the correctness of our privacy-preserving imputation methods, yielding the largest error around 3 × 10-3, closely matching plaintext methods. We also analyzed the scalability of our methods to varying numbers of samples, showing their applicability to real-world healthcare problems. Our analysis demonstrated that all our methods scale linearly with the number of samples. Except for kNN, the runtime of all our methods indicates that they can be utilized for large datasets. © 2024 IEEE.

关键词： Differential privacy

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FHAUC: privacy preserving AUC Calculation for Federated learning using Fully Homomorphic Encryption

arXiv

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arXiv 2024年

作者： Baykara, Cem Ata Ünal, Ali Burak Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning University of Tübingen Germany Institute for Bio-Informatics and Medical Informatics University of Tübingen Germany

Ensuring data privacy is a significant challenge for machine learning applications, not only during model training but also during evaluation. Federated learning has gained significant research interest in recent years as a result. Current research on federated learning primarily focuses on preserving privacy during the training phase. However, model evaluation has not been adequately addressed, despite the potential for significant privacy leaks during this phase as well. In this paper, we demonstrate that the state-of-the-art AUC computation method for federated learning systems, which utilizes differential privacy, still leaks sensitive information about the test data while also requiring a trusted central entity to perform the computations. More importantly, we show that the performance of this method becomes completely unusable as the data size decreases. In this context, we propose an efficient, accurate, robust, and more secure evaluation algorithm capable of computing the AUC in horizontal federated learning systems. Our approach not only enhances security compared to the current state-of-the-art but also surpasses the state-of-the-art AUC computation method in both approximation performance and computational robustness, as demonstrated by experimental results. To illustrate, our approach can efficiently calculate the AUC of a federated learning system involving 100 parties, achieving 99.93% accuracy in just 0.68 seconds, regardless of data size, while providing complete data privacy. Copyright © 2024, The Authors. All rights reserved.

关键词： privacy-preserving techniques

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Robust Representation learning for privacy-preserving machine learning: A Multi-Objective Autoencoder Approach

arXiv

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arXiv 2023年

作者： Ouaari, Sofiane Ünal, Ali Burak Akgün, Mete Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tuebingen Germany University of Tuebingen Germany Medical Data Privacy and Privacy Preserving Machine Learning University of Tuebingen Germany

Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and privacy and growing awareness of the need for privacy-preserving machine learning (ppML). Current ppML techniques utilize methods that are either purely based on cryptography, such as homomorphic encryption, or that introduce noise into the input, such as differential privacy. The main criticism given to those techniques is the fact that they either are too slow or they trade off a model's performance for improved confidentiality. To address this performance reduction, we aim to leverage robust representation learning as a way of encoding our data while optimising the privacy-utility trade-off. Our method centers on training autoencoders in a multi-objective manner and then concatenating the latent and learned features from the encoding part as the encoded form of our data. Such a deep learning-powered encoding can then safely be sent to a third party for intensive training and hyperparameter tuning. With our proposed framework, we can share our data and use third party tools without being under the threat of revealing its original form. We empirically validate our results on unimodal and multimodal settings, the latter following a vertical splitting system and show improved performance over state-of-the-art. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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privacy-preserving Federated Unsupervised Domain Adaptation for Regression on Small-Scale and High-Dimensional Biological data

arXiv

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arXiv 2024年

作者： Baykara, Cem Ata Ünal, Ali Burak Pfeifer, Nico Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning University of Tübingen Tübingen72076 Germany Institute for Bio-Informatics and Medical Informatics University of Tübingen Tübingen72076 Germany

machine learning models often struggle with generalization in small, heterogeneous datasets due to domain shifts caused by variations in data collection and population differences. This challenge is particularly pronounced in biological data, where data is high-dimensional, small-scale, and decentralized across institutions. While federated domain adaptation methods (FDA) aim to address these challenges, most existing approaches rely on deep learning and focus on classification tasks, making them unsuitable for small-scale, high-dimensional applications. In this work, we propose freda, a privacy-preserving federated method for unsupervised domain adaptation in regression tasks. Unlike deep learning-based FDA approaches, freda is the first method to enable the federated training of Gaussian Processes to model complex feature relationships while ensuring complete data privacy through randomized encoding and secure aggregation. This allows for effective domain adaptation without direct access to raw data, making it well-suited for applications involving high-dimensional, heterogeneous datasets. We evaluate freda on the challenging task of age prediction from DNA methylation data, demonstrating that it achieves performance comparable to the centralized state-of-the-art method while preserving complete data privacy. © 2024, CC BY.

关键词： Federated learning

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A privacy-preserving Framework for Collaborative machine learning with Kernel Methods

A Privacy-Preserving Framework for Collaborative Machine Lea...

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IEEE International Conference on Trust, privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün Dept. of Computer Science Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML) University of Tübingen Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Germany

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise or entail significant computational overhead. An example for such a use case is machine learning on clinical data. To realize exact and efficient privacy preserving computation of kernel methods, we propose FLAKE, a Framework for learning with Anonymized KErnels on horizontally distributed data. With our method, the data sources mask their data so that a Gram matrix can be computed without compromising privacy or utility. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector machines. We prove that our framework prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. The conducted experiments on clinical, genomic, and image data provide confirmation that our approach is applicable across a wide range of settings. Additionally, our method outperforms comparable approaches in both computational efficiency and accuracy. Thus, FLAKE is a lightweight, applicable approach suitable for various use cases.

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