Homomorphic Encryption for Secure Analytics
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Solution Overview
Problem
Existing data processing systems face challenges in securely processing sensitive data across multiple sources with different security levels, as traditional encryption methods require significant computational resources and can cause delays, and may expose data to unauthorized access during transmission and processing.
Innovation Solution
The implementation of a method and system using homomorphic encryption, where a term generation function, hash function, and public key are used to encrypt and process queries, allowing secure analytics by generating and applying homomorphically encrypted lists of indices to data sets, ensuring that only the client can decrypt the results using a private key, thus maintaining data security throughout the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption methods are applied to sensitive data during transmission and processing, then data security is improved, but processing time increases and computational resources are consumed
Solution Approach 1:
The patent applies preliminary action by pre-generating homomorphic encryption keys and preparing encrypted query structures before actual data processing occurs. The system pre-computes encryption parameters and stores them in ready-to-use format, so that when queries need to be executed on sensitive data, the encryption framework is already in place, reducing the time penalty during actual processing operations.
Solution Approach 2:
The patent replaces traditional mechanical encryption/decryption operations with homomorphic encryption mathematics. Instead of encrypting data and then decrypting it after processing (which requires significant computational overhead), the system uses homomorphic properties that allow mathematical operations to be performed directly on encrypted data, substituting the need for time-consuming decryption-reencryption cycles with efficient cryptographic algebra operations.
2Reliability
If traditional encryption methods are applied to sensitive data during transmission and processing, then data security is improved, but computational resources are consumed
Solution Approach 1:
The patent replaces resource-intensive traditional encryption mechanics with homomorphic encryption mathematical operations. The system substitutes repeated encryption-decryption cycles with single-pass homomorphic operations that leverage algebraic properties, dramatically reducing CPU cycles and energy consumption while maintaining security.
Solution Approach 2:
The patent changes the cryptographic parameter space by using homomorphic encryption schemes with optimized key sizes and operation complexities. By selecting specific homomorphic encryption parameters (such as modulus sizes, polynomial degrees, and noise management strategies), the system achieves a balance between security strength and computational efficiency, reducing resource consumption compared to conventional approaches.
3Adaptability or versatility
If queries are processed by accessing data from various data sources with different levels of security, then query completeness is improved, but security management complexity increases
Solution Approach 1:
The patent applies universality by creating a unified homomorphic encryption framework that works across multiple data sources with different security levels. The same encryption scheme and operational protocol are used regardless of the source, allowing the system to query diverse databases (some encrypted, some partially encrypted, some unencrypted) through a single consistent interface, simplifying security management while maintaining query completeness.
Solution Approach 2:
The patent introduces homomorphic encryption as an intermediary layer between the query system and diverse data sources. This intermediary allows queries to be transformed into homomorphic operations that can safely interact with data at any security level without requiring complex point-to-point security negotiations, automatically managing the complexity of accessing heterogeneous secure and unsecure sources.
Data Source
AI summary
Provided are methods and systems for performing secure analytics using term generations and a homomorphic encryption. An example method includes receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, wherein the list of indices is generated using the term generation function and the hash function, applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices, extracting, by the server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the encrypted data set to obtain an encrypted result, and sending the encrypted result to the client to decrypt the encrypted result using a private key of the homomorphic encryption scheme.