1 research outputs found
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
To train sophisticated machine learning models one usually needs many
training samples. Especially in healthcare settings these samples can be very
expensive, meaning that one institution alone usually does not have enough on
its own. Merging privacy-sensitive data from different sources is usually
restricted by data security and data protection measures. This can lead to
approaches that reduce data quality by putting noise onto the variables (e.g.,
in -differential privacy) or omitting certain values (e.g., for
-anonymity). Other measures based on cryptographic methods can lead to very
time-consuming computations, which is especially problematic for larger
multi-omics data. We address this problem by introducing ESCAPED, which stands
for Efficient SeCure And PrivatE Dot product framework, enabling 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. We evaluated our framework on
drug resistance prediction for HIV-infected people and multi-omics
dimensionality reduction and clustering problems in precision medicine. In
terms of execution time, our framework significantly outperforms the
best-fitting existing approaches without sacrificing the performance of the
algorithm. Even though we only show the benefit for kernel-based algorithms,
our framework can open up new research opportunities for further machine
learning models that require the dot product of vectors from multiple sources.Comment: AAAI 2021, Preprint version of the full paper with supplementary
materia