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

arxiv:2309.04427 (2023)

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

arxiv:2306.02677 (2023)

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

arXiv:2202.03023 (2022)

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

Bioinformatics 38,8 (2022)

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

AAAI 35, 11 (2021)

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

Bioinformatics 36,21 (2021)