MLCryptoBox : cryptographic toolbox for distributed machine learning
Author
Ishaq, MuhammadOther Contributors
Magdon-Ismail, Malik; Milanova, Ana; Varela, Carlos A.;Date Issued
2018-05Subject
Computer scienceDegree
MS;Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.; Attribution-NonCommercial-NoDerivs 3.0 United StatesMetadata
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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.;Description
May 2018; School of ScienceDepartment
Dept. of Computer Science;Publisher
Rensselaer Polytechnic Institute, Troy, NYRelationships
Rensselaer Theses and Dissertations Online Collection;Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.;Collections
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.