Selfiie Corporation

Selfiee Corporation

Gharib Gharibi

Riddhiman Das

Greg Storm

Ravi Patel

Andrew Rademacher

A system and method for dividing a model between a client and server for inference. The method include dividing, at a first computing device, a model into a first portion of the model and a second portion of the model, transmitting the second portion to a second computing device, wherein the second computing device provides private data the second portion, receiving a activation values at the first computing device from the second computing device, applying the activation values to the first portion and transmitting a model output from the first computing device to the second computing device. The model can also be divided into three or more parts.

Systems and methods for providing a split inference approach to protect data and model

A system and method are disclosed for providing a private multi-modal artificial intelligence platform. The method includes splitting a neural network into a first client-side network, a second client-side network and a server-side network and sending the first client-side network to a first client. The first client-side network processes first data from the first client, the first data having a first type. The method includes sending the second client-side network to a second client. The second client-side network processes second data from the second client, the second data having a second type. The first type and the second type have a common association. Forward and back propagation occurs between the client side networks and disparate data types on the different client side networks and the server-side network to train the neural network.

Systems and methods for blind multimodal learning

A system and method are disclosed for providing an artificial intelligence platform. An example method includes examining part of a global neural network to locate a split layer in the global neural network, creating an equivalent model to the part of the global neural network of a same size but having opposite operations, generating smashed data based on an operation on input data by the part of the global neural network, training the equivalent model by inputting the smashed data to generate a second a mirrored copy of the input data, quantifying a distance between the input data and the second generated set of mirrored data to yield a value and, when the value is below a threshold, determining that a current location of the split layer in the global neural network is safe for a training process.

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Overland Park, MO, United States of America

Gharib Gharibi