At the end of this page, you can find the full list of publications.
Leveraging the power of robust representation learning, our novel framework advances the field of privacy-preserving machine learning (ppML). Traditional ppML techniques either compromise speed or model performance to safeguard data privacy. Our solution employs multi-objective-trained autoencoders to optimize the balance between data utility and privacy. By sharing only the encoded data form, we enable secure utilization of third-party services for intensive model training and hyperparameter tuning. Our empirical validation, across both unimodal and multimodal settings makes data sharing both efficient and confidential.
S Ouaari, AB Ünal, M Akgün, N Pfeifer
We’ve introduced FLAKE, a privacy-preserving Federated Learning Approach for Kernel methods on horizontally distributed data. By allowing data sources to mask their data, a centralized instance can generate a Gram matrix, preserving privacy while enabling the training of kernel-based algorithms like Support Vector Machines. We’ve established that FLAKE safeguards against semi-honest adversaries learning the input data or the number of features. Testing on clinical and synthetic data confirms FLAKE’s superior accuracy and efficiency over similar methods. Its data masking and Gram matrix computation times are significantly less than SVM training times, making it highly applicable across various use cases.
A Hannemann, AB Ünal, A Swaminathan, E Buchmann, M Akgün
We propose a secure 3-party computation framework, CECILIA, offering PP building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix.
AB Ünal, N Pfeifer, M Akgün
Disease–gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. We propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner.
M Akgün, N Pfeifer, O Kohlbacher
We introduce ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. ESCAPED enables the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise
AB Ünal, M Akgün, N Pfeifer
We present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private.
M Akgün, AB Ünal, B Ergüner, N Pfeifer, O Kohlbacher
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
S Ouaari, AB Ünal, M Akgün, N Pfeifer
arxiv:2309.04427 (2023)
A Privacy-Preserving Federated Learning Approach for Kernel methods
A Hannemann, AB Ünal, A Swaminathan, E Buchmann, M Akgün
arxiv:2306.02677 (2023)
CECILIA: Comprehensive Secure Machine Learning Framework
AB Ünal, N Pfeifer, M Akgün
arXiv:2202.03023 (2022)
Efficient privacy-preserving whole-genome variant queries
M Akgün, N Pfeifer, O Kohlbacher
Bioinformatics 38,8 (2022)
Escaped: Efficient secure and private dot product framework for kernel-based machine learning algorithms with applications in healthcare
AB Ünal, M Akgün, N Pfeifer
AAAI 35, 11 (2021)
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves with Secure 3-Party Computation
AB Ünal, N Pfeifer, M Akgün
arXiv:2102.08788 (2021)
Identifying disease-causing mutations with privacy protection
M Akgün, AB Ünal, B Ergüner, N Pfeifer, O Kohlbacher
Bioinformatics 36,21 (2021)