Homomorphic Encryption Hyperdimensional Computing Privacy
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Solution Overview
Problem
Conventional homomorphic encryption methods face challenges in ensuring data privacy and performance, particularly when dealing with large-scale datasets or complex computations, leading to suboptimal efficiency and scalability.
Innovation Solution
The implementation of a privacy-preserving machine learning system using homomorphic encryption (HE) techniques, which enables a server to perform hyperdimensional computing processes, such as similarity searches, on encrypted input query vectors without decrypting them, thereby maintaining data confidentiality and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional homomorphic encryption methods are used to perform computations on encrypted data, then data privacy is preserved, but computational overhead increases and efficiency decreases
Solution Approach 1:
The patent segments the homomorphic encryption process into distinct phases: key generation, data encryption, encrypted computation execution, and result decryption. By dividing the complex encryption workflow into manageable segments with optimized operations at each stage, the system maintains security while improving overall computational efficiency.
Solution Approach 2:
The patent employs parameter optimizations in the homomorphic encryption scheme, including modulus selection, polynomial degree configuration, and noise management parameters. These parameter changes enable faster cryptographic operations while maintaining the security guarantees of homomorphic encryption.
2Reliability
If conventional homomorphic encryption methods are used for large-scale datasets, then data confidentiality is maintained, but resource requirements increase
Solution Approach 1:
The patent transitions from traditional vertical computation approaches to a horizontal dimension by implementing parallel encrypted computation across multiple processing units. This dimensional shift allows large-scale datasets to be processed simultaneously in an encrypted state, reducing resource requirements while maintaining confidentiality.
3Productivity
If complex homomorphic operations are performed on encrypted data, then computation results are obtained, but operational complexity increases
Solution Approach 1:
The patent introduces intermediary structures including pre-computed lookup tables, intermediate ciphertext representations, and buffer zones for noise management. These intermediaries simplify complex homomorphic operations by breaking them into smaller, more manageable steps with reduced operational complexity.
Data Source
AI summary
Provided is a homomorphic encryption (HE) operation method. The HE operation method includes receiving an encrypted input query hyperdimensional vector (HV) from a client based on an HE technique, performing a hyperdimensional computing process including a similarity search operation on the encrypted input query HV, and transmitting a result of the hyperdimensional computing process to the client.