One-time pad system and method for secured and private on-cloud machine learning services

Patent metadata

Field	Value
Publication number	US-12567957-B2
Application number	US-202418645954-A
Country	US
Kind code	B2
Filing date	Apr 25, 2024
Priority date	Oct 28, 2021
Publication date	Mar 3, 2026
Grant date	Mar 3, 2026

Abstract

An organization's system is configured to label a given document based on an on-cloud classification service, while maintaining confidentiality of the document's content from all entities external to the organization, including: (a) an encoder configured to receive the given document, and to create an embedding of the given document; (b) a deconvolution unit having a neural network, wherein weights of neurons within the neural network are defined relative to a key, the deconvolution unit is configured to receive the embedding, deconvolve the embedding, thereby to create a scrambled document which is then sent to the on-cloud classification service; (c) a pre-trained internal inference network, configured to: (i) receive from the on-cloud service a cloud-classification of the scrambled document, (ii) to also receive a copy of the embedding, and (ii) to identify, given the received cloud-classification and the embedding copy, a true label of the given document.

First claim

The invention claimed is:

1 . An organization's system configured to label a given document based on an on-cloud classification service, while maintaining confidentiality of the given document's content from all entities external to the organization, comprising:
a. an encoder configured to receive said given document, and to create an embedding of the given document; b. a deconvolution unit having a neural network, wherein weights of neurons within the neural network are defined relative to a key, said deconvolution unit being configured to receive said embedding, deconvolve the embedding, thereby to create a scrambled document which is then sent to the on-cloud classification service; and c. a pre-trained internal inference network, configured to: (i) receive from said on-cloud classification service a cloud-classification of said scrambled document; (ii) also receive a copy of said embedding; and (iii) identify, given said received cloud-classification and said embedding copy, said label of said given document.

2 . The system of claim 1 , wherein said embedding is a reduced size of said given document, and wherein said scrambled document is of increased size compared to said embedding.

3 . The system of claim 1 , wherein a type of said given document is selected from text, table, and image.

4 . The system of claim 1 , wherein the internal inference network is a machine-learning network that is trained by: (i) a plurality of documents and respective true labels, and (ii) a plurality of respective cloud classifications resulting from submission each of the plurality of said documents, respectively, to a portion of the system that includes said encoder, said deconvolution unit, and said cloud classification service.

5 . The system of claim 1 , wherein said key is periodically altered, and wherein said internal inference network is re-trained upon each key alteration.

6 . The system of claim 1 , particularly adapted for labeling a text document, wherein:
said text document is separated into a plurality of sentences; each sentence is inserted separately into said encoder as a given document; and said pre-trained internal inference network identifies a true label of each said sentences, respectively.

7 . The system of claim 1 , particularly adapted for labeling a given table-type document, wherein:
said encoder has the form of a row/tuple to image converter; said encoder receives at its input separately each row of said given table-type document; and said pre-trained internal inference network identifies a true label of each said rows, respectively.

8 . The system of claim 1 , wherein:
additional documents, whose labels are known, respectively, are fed into said encoder, in addition to said given document; a concatenation unit is used to concatenate distinct embeddings created by the encoder for said given document and said additional documents, thereby forming a combined vector V; said combined vector V is fed into said deconvolution unit; and said pre-trained internal inference network is further configured to: (i) receive, in addition to the copy of said embedding, a label of each said additional documents; and (ii) identify said true label of said given document based on the labels of each said additional documents, in addition to said received cloud-classification, and said embedding copy.

9 . A method enabling an organization to label a given document based on an on-cloud classification service, while maintaining confidentiality of the given document's content from all entities external to the organization, comprising:
a. encoding said given document, resulting in an embedding of the given document; b. deconvolving said embedding by use of a deconvolution unit comprising a neural network, wherein weights of neurons within the neural network are defined relative to a key, thereby to create a scrambled document, and sending the scrambled document to the on-cloud classification service; c. using a pre-trained internal inference network to: (a) receive from said on-cloud service a cloud-classification of said scrambled document, (b) to also receive a copy of said embedding, and (c) to identify, given said received cloud-classification and said embedding copy, a true label of said given document.

10 . The method of claim 9 , wherein said embedding is a reduced size of said document, and wherein said scrambled document is of increased size compared to said embedding.

11 . The method of claim 9 , wherein a type of said given document is selected from text, table, and image.

12 . The method of claim 9 , wherein the internal inference network is a machine-learning network that is trained by (i) a plurality of documents and respective true labels, and (ii) a plurality of cloud classifications resulting from said encoding, deconvolution, and transfer of same documents, respectively, through said cloud classification service.

13 . The method of claim 9 , further comprising periodically altering said key, and further re-training said internal inference network upon each key alteration.

14 . A multi-organization system for commonly training a common on-cloud classification service by labeled given documents submitted from all organizations, while maintaining confidentiality of the documents' contents of each organization from all entities external to that organization, comprising:
a training sub-system in each organization comprising: a. an encoder configured to receive a given document, and to create an embedding of the given document; b. a deconvolution unit having a neural network, wherein weights of neurons within the neural network are defined relative to a key, said deconvolution unit being configured to receive said embedding, deconvolve the embedding, thereby to create a scrambled document which is then sent for training to the common on-cloud classification service, together with the respective label of that given document.

15 . The multi-organization system of claim 14 , wherein upon completion of the common training by labeled documents from all organizations, said common on-cloud classification service is ready to provide confidential documents classification to each said organizations.

16 . The multi-organization system of claim 14 , wherein during real-time labeling of new documents, each organization's sub-system comprising:
a. an encoder configured to receive a new un-labeled document, and to create an embedding of the new document; b. a deconvolution unit having a neural network, wherein weights of neurons within the neural network are defined relative to a key, said deconvolution unit being configured to receive said embedding, deconvolve the embedding, thereby to create a scrambled document which is then sent to the on-cloud classification service; c. a pre-trained internal inference network, configured to: (a) receive from said on-cloud service a common cloud-classification vector of said scrambled document, (b) to also receive a copy of said embedding, and (c) to identify, given said received common cloud-classification vector and said embedding copy, a true label of said un-labeled document.

17 . The system of claim 14 , wherein said embedding is a reduced size of said new document, and wherein said scrambled image is of increased size compared to said embedding.

18 . The system of claim 14 , wherein a type of said document is selected from text, table, and image.

19 . The system of claim 16 , wherein the internal inference network of each organization is a machine-l

Assignees

• B G Negev Technologies And Applications Ltd At Ben Gurion Univ

Inventors

• Katz Gilad

Classifications

• H04L9/0662

  with particular pseudorandom sequence generator · CPC title

• G09C5/00

  Ciphering apparatus or methods not provided for in the preceding groups, e.g. involving the concealment or deformation of graphic data such as designs, written or printed messages · CPC title

• H04L9/0894

  Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage · CPC title

• H04L9/0861 Primary

  Generation of secret information including derivation or calculation of cryptographic keys or passwords · CPC title

• G06F21/6245 Primary

  Protecting personal data, e.g. for financial or medical purposes · CPC title