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公开(公告)号:US20230006980A1
公开(公告)日:2023-01-05
申请号:US17939797
申请日:2022-09-07
申请人: TripleBlind, Inc.
IPC分类号: H04L9/40 , G06K9/62 , G06N3/04 , G06N3/08 , G06Q30/06 , H04L9/00 , H04L9/06 , G06Q20/40 , G06F17/16
摘要: A system and method for training a decision tree are disclosed. A method includes publishing, by a first party, a first set of nominated cut-off values at a current node of a decision tree to be trained, computing a first respective impurity value for the first set of nominated cut-off values at the current node, creating first respective n shares of the first respective impurity value, transmitting, from the first party and so a second party, one of the first respective n shares of the first respective impurity value, receiving from the second party one of a second respective n shares of the second respective impurity value, adding a group of impurity values to yield a combined impurity value based on the one of the first respective n shares and the one of the second respective n shares and determining, based on the combined impurity value, a best threshold.
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公开(公告)号:US11363002B2
公开(公告)日:2022-06-14
申请号:US16828420
申请日:2020-03-24
申请人: TripleBlind, Inc.
IPC分类号: G06F17/16 , H04L9/00 , H04L9/06 , G06Q20/40 , G06Q30/06 , H04L9/40 , G06Q20/12 , G06K9/62 , G06N3/04 , G06N3/08
摘要: 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.
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公开(公告)号:US11843587B2
公开(公告)日:2023-12-12
申请号:US17939285
申请日:2022-09-07
申请人: TripleBlind, Inc.
IPC分类号: H04L9/40 , G06N3/082 , H04L9/00 , G06F17/16 , G06N3/04 , H04L9/06 , G06F18/24 , G06F18/2113 , G06N3/098 , G06N3/048 , G06F16/13 , G06F21/62
CPC分类号: H04L63/0428 , G06F16/13 , G06F17/16 , G06F18/2113 , G06F18/24 , G06F21/6245 , G06N3/04 , G06N3/048 , G06N3/082 , G06N3/098 , H04L9/008 , H04L9/0625 , H04L2209/46
摘要: 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.
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公开(公告)号:US11843586B2
公开(公告)日:2023-12-12
申请号:US17897884
申请日:2022-08-29
申请人: TripleBlind, Inc.
发明人: Gharib Gharibi , Ravi Patel , Babak Poorebrahim Gilkalaye , Praneeth Vepakomma , Greg Storm , Riddhiman Das
IPC分类号: H04L9/40 , G06F17/16 , H04L9/00 , H04L9/06 , G06N3/04 , G06N3/082 , G06Q20/40 , G06Q30/0601 , G06F18/24 , G06F18/2113
CPC分类号: H04L63/0428 , G06F17/16 , G06F18/2113 , G06F18/24 , G06N3/04 , G06N3/082 , G06Q20/401 , G06Q30/0623 , H04L9/008 , H04L9/0625 , G06Q2220/00 , H04L2209/46
摘要: 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.
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公开(公告)号:US11792646B2
公开(公告)日:2023-10-17
申请号:US17874599
申请日:2022-07-27
申请人: TRIPLEBLIND, INC.
发明人: Babak Poorebrahim Gilkalaye , David Norman Wagner , Riddhiman Das , Andrew James Rademacher , Craig Gentry , Gharib Gharibi , Greg Storm , Stephen Scott Penrod
CPC分类号: H04W12/06 , G06F9/547 , G06F21/6218
摘要: 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.
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公开(公告)号:US11582203B2
公开(公告)日:2023-02-14
申请号:US16828085
申请日:2020-03-24
申请人: TripleBlind, Inc.
IPC分类号: H04L9/40 , G06F17/16 , H04L9/00 , H04L9/06 , G06Q20/40 , G06Q30/06 , G06Q20/12 , G06K9/62 , G06N3/04 , G06N3/08 , G06Q30/0601 , G06N3/082
摘要: 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.
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公开(公告)号:US12088565B2
公开(公告)日:2024-09-10
申请号:US17939585
申请日:2022-09-07
申请人: TripleBlind, Inc.
IPC分类号: G06F16/00 , G06F17/16 , G06F18/2113 , G06F18/24 , G06N3/04 , G06N3/082 , G06Q20/40 , G06Q30/0601 , H04L9/00 , H04L9/06 , H04L9/40
CPC分类号: H04L63/0428 , G06F17/16 , G06F18/2113 , G06F18/24 , G06N3/04 , G06N3/082 , G06Q20/401 , G06Q30/0623 , H04L9/008 , H04L9/0625 , G06Q2220/00 , H04L2209/46
摘要: 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.
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公开(公告)号:US11991156B2
公开(公告)日:2024-05-21
申请号:US17939224
申请日:2022-09-07
申请人: TripleBlind, Inc.
IPC分类号: H04L9/40 , G06F16/13 , G06F17/16 , G06F18/2113 , G06F18/24 , G06F21/62 , G06N3/04 , G06N3/048 , G06N3/082 , G06N3/098 , G06Q20/40 , G06Q30/0601 , H04L9/00 , H04L9/06
CPC分类号: H04L63/0428 , G06F16/13 , G06F17/16 , G06F18/2113 , G06F18/24 , G06F21/6245 , G06N3/04 , G06N3/048 , G06N3/082 , G06N3/098 , G06Q20/401 , G06Q30/0623 , H04L9/008 , H04L9/0625 , G06Q2220/00 , H04L2209/46
摘要: 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 ki=gri mod p and transmitting the set of respective values ki to 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.
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公开(公告)号:US20230244650A1
公开(公告)日:2023-08-03
申请号:US18296689
申请日:2023-04-06
申请人: TripleBlind, Inc.
IPC分类号: G06F16/22 , G06F16/2453 , H04L9/32
CPC分类号: G06F16/2255 , G06F16/24545 , H04L9/3236 , G06F16/24544 , H04L2209/46
摘要: 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.
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公开(公告)号:US20230006978A1
公开(公告)日:2023-01-05
申请号:US17939285
申请日:2022-09-07
申请人: TripleBlind, Inc.
IPC分类号: H04L9/40 , G06K9/62 , G06N3/04 , G06N3/08 , G06Q30/06 , H04L9/00 , H04L9/06 , G06Q20/40 , G06F17/16
摘要: 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.
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