Tripleblind, Inc.

Tripleblind Holding Company

Selfiie Corporation

Triplelind Holdings, Inc.

Selfiee Corporation

Riddhiman Das

Babak Poorebrahim Gilkalaye

Greg Storm

Gharib Gharibi

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.

Systems and methods for quantifying data leakage from a split layer

A system and method are disclosed for training a recommendation system. The method includes initiating, at a server device, an item-vector matrix V, wherein the item-vector matrix V includes a value m related to a total number of items across one or more client devices and a value d representing a hidden dimension, transmitting the item-vector matrix V to each client device, wherein each client device trains a local matrix factorization model using a respective user vector U and the item-vector matrix V to generate a respective set of gradients on each respective client device, receiving, via a secure multi-party compute protocol, and from each client device, the respective set of gradients, updating the item-vector matrix V using the respective set of gradients from each client device to generate an updated item-vector matrix V and downloading the updated item-vector matrix V to at least one client device.

Systems and methods for privacy preserving training and inference of decentralized recommendation systems from decentralized data

Systems, methods, and computer-readable media for achieving privacy for both data and an algorithm that operates on the data. A system can involve receiving an algorithm from an algorithm provider and receiving data from a data provider, dividing the algorithm into a first algorithm subset and a second algorithm subset and dividing the data into a first data subset and a second data subset, sending the first algorithm subset and the first data subset to the algorithm provider and sending the second algorithm subset and the second data subset to the data provider, receiving a first partial result from the algorithm provider based on the first algorithm subset and first data subset and receiving a second partial result from the data provider based on the second algorithm subset and the second data subset, and determining a combined result based on the first partial result and the second partial result.

Systems and methods for encrypting data and algorithms

A method includes receiving, on a computer-implemented system and from user, an identification of data and an identification of an algorithm and, based on a user interaction with the computer-implemented system comprising a one-click interaction or a two-click interaction. Without further user input, the method includes dividing the data into a data first subset and a data second subset, dividing the algorithm (or a Boolean logic gate representation of the algorithm) into an algorithm first subset and an algorithm second subset, running, on the computer-implemented system at a first location, the data first subset with the algorithm first subset to yield a first partial result, running, on the computer-implemented system at a second location separate from the first location, the data second subset with the algorithm second subset to yield a second partial result and outputting a combined result based on the first partial result and the second partial result.

Systems and methods for providing a marketplace where data and algorithms can be chosen and interact via encryption

A system and method are disclosed for providing an averaging of models for federated learning and blind learning systems. The method includes selecting, at a server, a generator g and a number p, transmitting, to at least two n client devices, the generator g and the number p, receiving, from each client device i of the at least two client devices, a respective value k=gmod p and transmitting the set of respective values kto each client device i of the at least two client devices where respective added group of shares are generated on each client device i. The method includes receiving each respective added group of shares from each client device i of the at least two client devices and adding all the respective added group of shares to make a global sum of shares and dividing the global sum of shares by n.

Systems and methods for secure averaging of models for federated learning and blind learning using secure multi-party computation

Disclosed is a process for testing a suspect model to determine whether it was derived from a source model. An example method includes receiving, from a model owner node, a source model and a fingerprint associated with the source model, receiving a suspect model at a service node, based on a request to test the suspect model, applying the fingerprint to the suspect model to generate an output and, when the output has an accuracy that is equal to or greater than a threshold, determining that the suspect model is derived from the source model. Imperceptible noise can be used to generate the fingerprint which can cause predictable outputs from the source model and a potential derivative thereof.

Systems and methods for providing a systemic error in artificial intelligence algorithms

A method includes dividing a plurality of filters in a first layer of a neural network into a first set of filters and a second set of filters, applying each of the first set of filters to an input of the neural network, aggregating, at a second layer of the neural network, a respective one of a first set of outputs with a respective one of a second set of outputs, splitting respective weights of specific neurons activated in each remaining layer, at each specific neuron from each remaining layer, applying a respective filter associated with each specific neuron and a first corresponding weight, obtaining a second set of neuron outputs, for each specific neuron, aggregating one of the first set of neuron outputs with one of a second set of neuron outputs and generating an output of the neural network based on the aggregated neuron outputs.

Growing community of inventors

Parkville, MO, United States of America

Riddhiman Das