Tripleblind, Inc.

Advanced New Technologies Co., Ltd.

Qualcomm Incorporated

Alibaba Group Holding Limited

Eyeverify Inc.

Riddhiman Das

Babak Poorebrahim Gilkalaye

Greg Storm

Gharib Gharibi

Ravi Patel

Gregory Lee Storm

Zikomo Fields

Praneeth Vepakomma

Andrew James Rademacher

David Norman Wagner

Reza R Derakhshani

Robert Ganton

Rajeev Durai Rajan

Casey Hughlett

Matthew Barrow

Toby Rush

Kabir Kasargod

Jatin Kadakia

Thien Lee

Mark Jerger

Christopher Talbot

Steve Penrod

Mitchell Roberts

Stephen Scott Penrod

Kumar Senthil

Craig Gentry

Vishwajeet Lohakarey

The disclosed concepts achieve privacy for data operated on by an algorithm in an efficient manner A method includes receiving a first algorithm subset, receiving a second algorithm subset, generating two shares of a first mathematical set based on the first algorithm subset and transmitting the two shares of the first mathematical set from a first entity to a second entity. The method can include generating two shares of a second mathematical set based on the second algorithm subset, transmitting the two shares of the second mathematical set from the second entity to the first entity, receiving first split data subset of a full data set and receiving a second split data subset of the full data set. The system, based on these subsets of data, generates a first output subset and a second output subset which are combined for the final output.

Systems and methods for efficient computations on split data and split algorithms

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

Disclosed is a method that includes training, at a client, a part of a deep learning network up to a split layer of the client. Based on an output of the split layer, the method includes completing, at a server, training of the deep learning network by forward propagating the output received at a split layer of the server to a last layer of the server. The server calculates a weighted loss function for the client at the last layer and stores the calculated loss function. After each respective client of a plurality of clients has a respective loss function stored, the server averages the plurality of respective weighted client loss functions and back propagates gradients based on the average loss value from the last layer of the server to the split layer of the server and transmits just the server split layer gradients to the respective clients.

Systems and methods for providing a modified loss function in federated-split learning

A system and method for securely computing an inference of two types of tree-based models, namely XGBoost and Random Forest, using secure multi-party computation protocol. The method includes computing a respective comparison result of each respective node of a plurality of nodes in a tree classifier. Each node has a respective threshold value. The respective comparison result is based on respective data associated with a data owner device being applied to a respective node having the respective threshold value. The method includes computing, based on the respective comparison result, a leaf value associated with the tree classifier, generating a share of the leaf value and transmitting, to the data owner device, a share of the leaf value. The data owner device computes, using a secure multi-party computation and between the model owner device and the data owner device, the leaf value for the respective data of the data owner.

Systems and methods for tree-based model inference using multi-party computation

A system and method are disclosed for secure multi-party computations. The system performs operations including establishing an API for coordinating joint operations between a first access point and a second access point related to performing a secure prediction task in which the first access point and the second access point will perform private computation of first data and second data without the parties having access to each other's data. The operations include storing a list of assets representing metadata about the first data and the second data, receiving a selection of the second data for use with the first data, managing an authentication and authorization of communications between the first access point and the second access point and performing the secure prediction task using the second data operating on the first data.

Systems and methods for providing a multi-party computation system for neural networks

A method of providing blind vertical learning includes creating, based on assembled data, a neural network having n bottom portions and a top portion and transmitting each bottom portion of then bottom portions to a client device. The training of the neural network includes accepting a, output from each bottom portion of the neural network, joining the plurality of outputs at a fusion layer, passing the fused outputs to the top portion of the neural network, carrying out a forward propagation step at the top portion of neural network, calculating a loss value after the forward propagation step, calculating a set of gradients of the loss value with respect to server-side model parameters and passing subsets of the set of gradients to a client device. After training, the method includes combining the trained bottom portion from each client device into a combined model.

Systems and methods for blind vertical learning

A system and method are disclosed for comparing private sets of data. The method includes encoding first elements of a first data set such that each element of the first data set is assigned a respective number in a first table, encoding second elements of a second data set such that each element of the second data set is assigned a respective number in a second table, applying a private compare function to compute an equality of each row of the first table and the second table to yield an analysis and, based on the analysis, generating a unique index of similar elements between the first data set and the second data set.

Systems and methods for enabling two parties to find an intersection between private data sets without learning anything other than the intersection of the datasets

Disclosed is a method for each party of a group of m parties to be able to learn an Nth smallest value in a combined list. The method includes providing a value Rto a group of members; computing how many numbers are smaller than Rin a respective list of values for each respective member of the group of members; computing, a total number of smaller values (P); identifying a position of Rin a combined list of values comprising each respective list of values; when N=P+1, returning R; when N is greater than P+1, removing all values smaller than Rin their respective list of values and setting N=N&#x2212;(P+1); when N is less than P+1, removing all numbers bigger than Rin their respective list of value; and setting i=i+1.

Systems and methods for finding a value in a combined list of private values

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 system and method are disclosed for providing a quantum proof key exchange. The method includes generating at a first computing device a random bit a, encrypting ausing quantum-proof homomorphic encryption &#x3be; to yield &#x3be;(a), transmitting &#x3be;(a) to a second computing device, generating at the second computing device a random bit b, encrypting busing the quantum-proof homomorphic encryption &#x3be; to yield &#x3be;(b), transmitting &#x3be;(b) to the first computing device and generating a common key between the first computing device and the second computing device based on &#x3be;(a) and &#x3be;(b).

Systems and methods for providing a quantum-proof key exchange

A system and method are disclosed for each party of a group of m parties to be able to learn an Nth smallest value in a combined list of the values in which each party has separate lists of values. A method includes creating, by each party of a group of m parties, m lists of additive shares associated with each party's respective list of data, distributing, from each party to each other party in the group of m parties, m&#x2212;1 of the lists of additive shares to yield a respective combined list of additive shares Wobtained by each party of the m parties, receiving from a trusted party a list of additive shares Vassociated with a hot-code vector V, computing, in a shared space by each party, a respective Rvalue using a secure multiplication protocol and comparing, in the shared space, by each party and using secure multi-party comparison protocol, the respective Rto all elements in the respective combined list of additive shares Wto yield a total number Pof values in Wthat are smaller than R. The value Pis used to either end the method or loop back for further processing with new values of Wand in some cases a new value of N.

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

Disclosed is a system and method of de-identifying data. A method includes splitting, at a first entity, a byte of data of an original record into a first random portion and a second random portion, inserting first random bits into the first random portion to yield a first new byte and inserting second random bits into the second random portion to yield a second new byte. The method then includes transmitting the second new byte to a second entity, receiving, at the first entity, a first portion of an algorithm from the second entity and processing the first new byte by the first portion of the algorithm to yield a first partial result. The first partial result can be combined with a second partial result from the second entity processing the second new byte by a second portion of the algorithm.

Systems and methods for providing a blind de-identification of privacy data

A system and method are disclosed for providing a privacy-preserving training approach for split learning methods, including blind learning. A method includes receiving, at a server device, encrypted smashed data from a client device, using a secure enclave on the server device, moving, on the server device, a server model, the encrypted smashed data and computer code for a blind learning operation into the secure enclave, performing, in the secure enclave, forward propagation using the decrypted smashed data to generate predicted values, comparing the predicted values to true labels using a loss function to yield a loss value, repeating the comparing step for all smashed data received at the server device from a plurality of clients to yield a plurality of loss values, averaging the plurality of loss values to yield an average loss value, updating model weights of the server model using the average loss value to yield gradients of the smashed data and transmitting the gradients of the smashed data to the client device.

Growing community of inventors

Kansas City, MO, United States of America

Riddhiman Das