Microsoft Technology Licensing, LLC

Divya Gupta

Nishanth Chandran

Rahul Anand Sharma

Muthian Sivathanu

Satyanarayana V Lokam

Sameer Wagh

Apurv Mehra

Sambhav Satija

Mayank Rathee

Nishant Kumar

Sudheesh Singanamalla

Deevashwer Rathee

Aseem Rastogi

A secure inference over Deep Neural Networks (DNNs) using secure two-party computation to perform privacy-preserving machine learning. The secure inference uses a particular type of comparison that can be used as a building block for various layers in the DNN including, for example, ReLU activations and divisions. The comparison securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by a provider of the DNN. Each party computing system parses their input into leaf strings of multiple bits. This is much more efficient than if the leaf strings were individual bits. Accordingly, the secure inference described herein is more readily adapted for using in complex DNNs.

Private inference in deep neural network

A system includes a set of low resource devices, each configured to receive transactions to be added to an encrypted block chain ledger from a sample of untrusted high resource devices, prepare a proposed block of the received transactions, provide the proposed block to the sample of untrusted high resource devices, receive proposed blocks from the untrusted high resource devices originating from the set of low resource devices. The low resource devices run a consensus protocol to select one proposed block to add to the encrypted block chain ledger stored on the untrusted high resource devices.

Lightweight blockchain based on split-trust

Systems and methods for private deep neural network training are disclosed. Method includes storing first private values at first machine and second private values at second machine; providing, to third machine, first share of first private values and first share of second private values; providing, to fourth machine, second share of first private values and second share of second private values; computing, at third machine, third machine-value based on first share of first private values and first share of second private values; computing, at fourth machine, fourth machine-value based on second share of first private values and second share of second private values; providing, to first machine and second machine, third machine-value and fourth machine-value; and computing, at first machine, a mathematical function of first private values and second private values, mathematical function being computed based on first private values stored at first machine, third machine-value, and fourth machine-value.

Private deep neural network training

Growing community of inventors

Bengaluru, India

Divya Gupta