MLCryptoBox : cryptographic toolbox for distributed machine learning
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Authors
Ishaq, Muhammad
Issue Date
2018-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
Abstract
We propose a hybrid design using multiparty computation (MPC) to achieve both accuracy and speed. An ML application should compute only those primitives cryptographically securely which operate on sensitive input. Towards this goal, we initiate development of a toolbox of special tailored cryptographic protocols for algebraic primitives. These are provided as a highly accessible blackbox for use by the ML community. Our focus is on speed, usability and extensibility.
Machine Learning (ML) is widely used in practice to build predictive models for various applications e.g. medicine, finance, advertisement, etc. For accuracy, these models have to be trained on large datasets. The problem is that, for reasons of privacy, legislation, competitive edge, etc., these datasets cannot be shared in the clear. Thus ML models should learn from these datasets without knowing what the actual data is. Traditional approaches towards a solution compromise on either speed or accuracy.
Machine Learning (ML) is widely used in practice to build predictive models for various applications e.g. medicine, finance, advertisement, etc. For accuracy, these models have to be trained on large datasets. The problem is that, for reasons of privacy, legislation, competitive edge, etc., these datasets cannot be shared in the clear. Thus ML models should learn from these datasets without knowing what the actual data is. Traditional approaches towards a solution compromise on either speed or accuracy.
Description
May 2018
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY