US 12,120,219 B2Granted

Machine learning-enabled character based encryption system

AssigneeSap SE

InventorRamalingam Tv

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Abstract

In an example embodiment, a solution is provided for encrypting and decrypting data in which the solution itself creates unique symbols, reducing or eliminating the possibility that a hacker or other malicious actor can understand what the symbols mean, let alone decrypt them. More particularly, for encryption, the characters of an original text is split into individual vector-based characters, and each of these vector-based characters are split into subcharacters at intersection points. Each of the split characters are then rotated, and the rotated characters are organized one on top of each other. The characters are then connected to each other, and the intersection points of the subcharacters within the characters that were used to split the characters into subcharacters are passed into a machine learning model that is trained to add lines between intersection points that have no lines, as noise to further obfuscate the original text.

Claims20

Dependent claims

A system comprising:

at least one hardware processor;
and
a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
accessing data, the data comprising one or more characters;
identifying a language of the data, the language being read in a first direction;
based on the language, retrieving a vector-based representation for each of the one or more characters;
rotating each vector-based representation to produce a set of rotated vector-based representations;
assigning an index value to each of the rotated vector-based representations, the index value based on a relative ordering of the one or more characters in the data for corresponding rotated vector-based representations;
drawing the rotated vector-based representations in a single drawing so that borders of successive rotated vector-based representations in the relative ordering overlap each other, and such that the rotated vector-based representations are drawn in the relative ordering in a second direction perpendicular to the first direction;
and
passing the drawing into a machine learning model, the machine learning model adding additional vectors to the drawing as noise, producing an encrypted drawing.

8 more elements

A method comprising:

accessing data, the data comprising one or more characters;
identifying a language of the data, the language being read in a first direction;
based on the language, retrieving a vector-based representation for each of the one or more characters;
rotating each vector-based representation to produce a set of rotated vector-based representations;
assigning an index value to each of the rotated vector-based representations, the index value based on a relative ordering of the one or more characters in the data for corresponding rotated vector-based representations;
drawing the rotated vector-based representations in a single drawing so that borders of successive rotated vector-based representations in the relative ordering overlap each other, and such that the rotated vector-based representations are drawn in the relative ordering in a second direction perpendicular to the first direction;
and
passing the drawing into a machine learning model, the machine learning model adding additional vectors to the drawing as noise, producing an encrypted drawing.

5 more elements

A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

accessing data, the data comprising one or more characters;
identifying a language of the data, the language being read in a first direction;
based on the language, retrieving a vector-based representation for each of the one or more characters;
rotating each vector-based representation to produce a set of rotated vector-based representations;
assigning an index value to each of the rotated vector-based representations, the index value based on a relative ordering of the one or more characters in the data for corresponding rotated vector-based representations;
drawing the rotated vector-based representations in a single drawing so that borders of successive rotated vector-based representations in the relative ordering overlap each other, and such that the rotated vector-based representations are drawn in the relative ordering in a second direction perpendicular to the first direction;
and
passing the drawing into a machine learning model, the machine learning model adding additional vectors to the drawing as noise, producing an encrypted drawing.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/987,425Filed 15 Nov 2022
Published: US 2024/0163079 A116 May 2024
Granted: 15 Oct 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G09C 5/00
Ciphering apparatus or methods not provided for in the preceding groups
H04L 9/0816
Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords: key establishment, i.e. cryptographic…
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/08
Neural networks: learning methods

Family

US 2024/0163079 A1Pre-grant publication · published 16 May 2024
US 12,120,219 B2This patent

From the US filing history. Foreign family members aren’t listed.

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