Homomorphic Matrix Multiplication for Secure Medical Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current homomorphic encryption schemes used by service providers are slow and memory-intensive due to their support for only plaintext operations with ciphertext, limiting the outsourcing of sensitive medical data to third parties like cloud service providers.
Innovation Solution
Performing homomorphic matrix multiplication on encrypted data, allowing computations to be conducted securely and efficiently without decryption, by encoding medical data as vectors and using a novel ciphertext packing method for high-throughput processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption schemes support only plaintext operations with ciphertext, then data privacy is protected, but computation speed and memory efficiency deteriorate
Solution Approach 1:
The patent segments the computation process into distinct phases: encryption of plaintext data, homomorphic matrix multiplication on ciphertexts, and decryption of results. This segmentation allows the system to maintain strong encryption during storage and transmission while enabling efficient matrix operations during computation, thus balancing privacy protection with computation speed.
Solution Approach 2:
The patent changes the operational parameters by supporting both plaintext and ciphertext operations within the same homomorphic encryption framework. By allowing matrix multiplication to be performed directly on ciphertexts without converting to plaintext, the system maintains encryption integrity while achieving computational efficiency, resolving the contradiction between privacy and speed.
2Device complexity
If homomorphic encryption schemes support only plaintext operations with ciphertext, then implementation simplicity is maintained, but memory consumption increases
Solution Approach 1:
The patent merges multiple operational capabilities (plaintext encryption, ciphertext matrix multiplication, and result decryption) into a unified homomorphic encryption system. This consolidation allows the system to perform complex matrix operations on encrypted data without requiring separate decryption and re-encryption steps, thereby reducing memory overhead while maintaining implementation feasibility.
3Adaptability or versatility
If third parties process encrypted medical data, then data sharing and analytics are enabled, but computation performance deteriorates
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that enables third parties to process medical data without accessing the actual plaintext. The encryption scheme acts as a mediator that preserves data privacy while allowing useful computations (matrix multiplications) to be performed on encrypted data, thus enabling data sharing without sacrificing computation performance.
Data Source
AI summary
A method, system and computer program product for performing computations on sensitive data while guaranteeing privacy. A service provider receives a first and a second ciphertext from a medical provider that homomorphically encrypts matrices A and B, respectively, using an encryption key, where the matrices A and B include medical data encoded as vectors. The service provider performs a homomorphic matrix multiplication on the first and second ciphertexts without decrypting the first and second ciphertexts. An encrypted result from the performed homomorphic matrix multiplication on the first and second ciphertexts is generated and transmitted to the medical provider to decrypt which matches a result of performing a matrix multiplication on unencrypted matrices A and B thereby enabling computations to be performed on the medical data in a secure manner.