US Patent Application 17715768. Shuffled Secure Multiparty Deep Learning simplified abstract
Contents
Shuffled Secure Multiparty Deep Learning
Organization Name
Inventor(s)
Andre Xian Ming Chang of Bellevue WA (US)
Shuffled Secure Multiparty Deep Learning - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17715768 Titled 'Shuffled Secure Multiparty Deep Learning'
Simplified Explanation
The abstract describes a method for protecting access to data samples in deep learning computations that are outsourced to external entities. This is done by dividing each data sample into randomized parts and shuffling these parts with parts from other data samples. The shuffled and randomized parts are then given to external entities to perform deep learning computations. The order of applying the summation and deep learning computation can be changed. The results obtained by the external entities can be shuffled back to their respective data samples for summation, providing the final result of the deep learning computation for each data sample.
Original Abstract Submitted
Protection of access to data samples in outsourcing deep learning computations via shuffling parts. For example, each data sample can be configured as the sum of a plurality of randomized parts. Parts from different data samples are shuffled to mix parts from different samples. One or more external entities can be provided with shuffled and randomized parts to generate results of applying a deep learning computation to the parts. The deep learning computation is configured to allow change of the order between applying the summation and applying the deep learning computation. Thus, results of the external entities applying the deep learning computation to their received parts can be shuffled back for the respective data samples for summation. The summation provides the result of applying the deep learning computation to a respective data sample.
