US Patent Application 17715877. Partition a Tensor with Varying Granularity Levels in Shuffled Secure Multiparty Computation simplified abstract
Contents
Partition a Tensor with Varying Granularity Levels in Shuffled Secure Multiparty Computation
Organization Name
Inventor(s)
Andre Xian Ming Chang of Bellevue WA (US)
Partition a Tensor with Varying Granularity Levels in Shuffled Secure Multiparty Computation - A simplified explanation of the abstract
- This abstract for appeared for US patent application number 17715877 Titled 'Partition a Tensor with Varying Granularity Levels in Shuffled Secure Multiparty Computation'
Simplified Explanation
The abstract describes a method for protecting access to a tensor (a mathematical object used in deep learning) when outsourcing computations to external entities. The tensor is divided into different portions, and computing tasks are generated to operate on these portions. The results of these tasks are then combined to obtain the final result of the computation. To prevent external entities from accessing or reconstructing the tensor, the computing tasks are shuffled and distributed out of order. This partitioning and shuffling technique ensures the security of the tensor during the outsourcing process.
Original Abstract Submitted
Protection of access to a tensor in outsourcing deep learning computations via shuffling. For example, the tensor in the computation of an artificial neural network can be partitioned into portions of different sizes. The computing tasks can be generated for operating on the portions such that the results of the computing tasks can be combined to obtain the result of a computing task operates on the tensor in the computation of the artificial neural network. The computing tasks can be shuffled for distribution out of order to external entities. The partitioning and shuffling can prevent the external entities from accessing and/or reconstructing the tensor.
