Homomorphic Encryption Using RNN Neuro-Topological Codes

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Solution Overview

Problem

Existing cryptographic encryption methods do not effectively allow secure computations on encrypted data without providing unrestricted access to the plaintext, especially in contexts requiring complex operations like deep learning and statistical analysis.

Innovation Solution

Homomorphic encryption using a tailored recurrent artificial neural network to encode plaintext as neuro-topological codes, allowing secure computations on encrypted data without revealing the underlying data, using a private key to tailor network responses and encode data patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional cryptographic encryption methods are used, then data security is maintained, but secure computations on encrypted data without plaintext access are not enabled

Engineering Contradiction:

Improvecapability to perform secure computations on encrypted dataVSAvoiddata security and privacy protection

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces homomorphic encryption as an intermediary mechanism that enables computations on encrypted data without requiring decryption. The encrypted data acts as a mediator between the need for secure storage and the need for computational processing, allowing third parties to perform statistical analyses and machine learning operations while the data remains encrypted throughout the entire process

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms traditional encryption parameters by implementing homomorphic properties that allow mathematical operations to be performed directly on ciphertext. This changes the fundamental parameters of the encryption system from purely protective to computationally enabling, while maintaining security through carefully controlled key management and encryption schemes

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If plaintext is provided for computations, then computation accuracy is improved, but data privacy and security are compromised

Engineering Contradiction:

Improvecomputation accuracyVSAvoiddata exposure and privacy loss

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

Homomorphic encryption serves as an intermediary that preserves computation accuracy while maintaining data privacy. The encrypted data structure allows mathematical operations to produce accurate results that, when decrypted, match the results of operations performed on plaintext, eliminating the need to expose actual data values during processing

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent creates and operates on copies of the data in encrypted form rather than the original plaintext. These ciphertext copies contain all necessary information for accurate computation while being mathematically transformed to prevent any direct revelation of the underlying sensitive data, allowing accurate analysis without accessing the actual patient information

Inventive Principle:

Principle #26Copying

3Productivity

If symmetric key encryption is used, then encryption and decryption efficiency is improved, but secure key exchange between parties becomes necessary

Engineering Contradiction:

Improveencryption and decryption speedVSAvoidsecure key exchange infrastructure

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements a homomorphic encryption system that serves multiple functions: it provides efficient encryption and decryption like symmetric key systems, enables secure third-party computations, and eliminates the need for separate secure key exchange infrastructure. The single encryption scheme handles both performance and security requirements across diverse computational scenarios

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Adaptability or versatility

If public key encryption is used, then secure key distribution is achieved, but decryption speed and computational efficiency deteriorate

Engineering Contradiction:

Improvesecure key distribution capabilityVSAvoiddecryption speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent segments the cryptographic functions by using homomorphic encryption properties that allow computation to occur in the encrypted domain without requiring expensive decryption operations. This separates the key distribution function (handled by the encryption scheme) from the computation function (handled by homomorphic operations), avoiding the need for slow public key decryption while maintaining secure key distribution

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12476787B2Homomorphic encryption

Publication Date: 2025.11.18 INAIT SA

AI summary

Methods, systems, and devices for homomorphic encryption. In one implementation, the methods include inputting first data into a recurrent artificial neural network, identifying patterns of activity in the recurrent artificial neural network that are responsive to the input of the secure data, storing second data representing whether the identified patterns of activity comports with topological patterns, and statistically analyzing the second data to draw conclusions about the first data.