171,219 research outputs found
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE
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
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is a distributed framework for training machine learning
models over the data residing at mobile devices, while protecting the privacy
of individual users. A major bottleneck in scaling federated learning to a
large number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with users achieves a secure aggregation overhead of
, as opposed to , while tolerating up to a user dropout
rate of . Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to speedup over the
state-of-the-art protocols with up to users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of
Turbo-Aggregate
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