Homomorphic Encryption Matrix Multiplication Medical Data Privacy
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Solution Overview
Problem
Existing methods for processing medical data in external resources, such as cloud computing, face challenges in anonymization and encryption, where anonymization may lose relevant information and encryption schemes like RSA are not semantically secure, making it difficult to use machine learning algorithms effectively while maintaining data privacy.
Innovation Solution
A homomorphic encryption scheme using matrix multiplication for encrypting and decrypting data, allowing computations on encrypted data without losing privacy, utilizing integer matrices for efficient encryption and decryption processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymization or pseudonymization is used to process medical data in external resources, then data privacy is protected, but relevant information may be lost
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that enables processing of encrypted medical data in external resources without decryption. The encryption scheme acts as a mediator that preserves both data privacy and information integrity, allowing cloud providers to perform computations on ciphertexts while maintaining the ability to retrieve accurate results after decryption, thus resolving the contradiction between privacy protection and information preservation
2Reliability
If RSA encryption is used to protect medical data, then data privacy is maintained, but the encryption is not semantically secure and vulnerable to attacks
Solution Approach 1:
The patent changes the fundamental parameters of the encryption scheme by transitioning from deterministic RSA encryption to a probabilistic homomorphic encryption system. This parameter change introduces semantic security while maintaining homomorphic properties, making the encryption resistant to attacks while enabling secure computation on encrypted medical data in external resources
3Adaptability or versatility
If deterministic RSA encryption is used, then homomorphic properties are maintained, but semantic security is compromised
Solution Approach 1:
The patent creates a composite encryption scheme that combines the homomorphic properties of deterministic encryption with the semantic security of probabilistic encryption. This composite approach integrates multiple encryption techniques to achieve both adaptability for machine learning computations and reliability for security, resolving the contradiction between maintaining homomorphic properties and ensuring semantic security
4Reliability
If medical data is encrypted before processing in external resources, then data privacy is protected, but machine learning algorithms cannot effectively process the encrypted data
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary that enables machine learning algorithms to process encrypted data directly without decryption. This mediator allows cloud-based machine learning models to perform computations on ciphertexts and produce encrypted results that, when decrypted, match the results of operations on plaintext, thus maintaining both privacy protection and processing efficiency
Data Source
AI summary
A computer-implemented method is for providing processed data. In an embodiment, the method includes receiving, by a first encryption entity, first plaintext data including a matrix of numbers; determining, by the first encryption entity, an encryption key including an integer matrix; homomorphically encrypting, by the first encryption entity, the first plaintext data based on a matrix multiplication of the first plaintext data and the encryption key, to generate first encrypted data; sending, by the first encryption entity, the first encrypted data to a processing entity; receiving, by a decryption entity, encrypted processed data from the processing entity, the encrypted processed data being based on the first encrypted data; decrypting, by the decryption entity, the encrypted processed data based on a matrix multiplication of the processed data and an inverse of the encryption key, to generate processed data; and providing, by the decryption entity, the processed data.