Homomorphic Encryption for Privacy-Preserving Data Relationship Computation
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Solution Overview
Problem
Existing methods fail to effectively compute and preserve privacy when determining relationships between data points distributed across different entities, as they often require centralization or compromise data security.
Innovation Solution
A method using homomorphic encryption and random noise to compute relationships between encrypted datasets, allowing for privacy-preserving data analysis without revealing sensitive information, by encrypting data with a public key and adding random noise to the results, which is then decrypted by the data owner using their private key.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is centralized for relationship computation, then computation capability is improved, but data security and privacy are compromised
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that enables a third-party server to perform relationship computations on encrypted data without accessing the plaintext. The server acts as a mediator that processes data in encrypted form, returning encrypted results that the original data owner can decrypt. This resolves the contradiction by providing computation capability through the server while maintaining data security through encryption, as the server never sees the actual data values.
2Reliability
If data is encrypted for privacy preservation, then data security is improved, but computation on the data becomes difficult
Solution Approach 1:
The patent replaces traditional mechanical computation (performing calculations on plaintext data) with cryptographic-based computation (performing calculations on ciphertext data using homomorphic encryption properties). Instead of decrypting data to compute relationships, the system uses mathematical properties of homomorphic encryption to perform computations directly on encrypted data, then decrypts only the final result. This substitution maintains data security while enabling computation through cryptographic mechanisms rather than traditional data processing.
3Measurement precision
If exact relationship computation is performed on encrypted data, then measurement precision is improved, but privacy protection is weakened
Solution Approach 1:
The patent changes the parameter of computation result precision by introducing controlled noise into the encrypted data during the relationship computation process. This differential privacy approach adds randomness to the computation results, preventing exact reconstruction of individual data points while maintaining statistical accuracy of aggregate relationships. The noise parameter is carefully calibrated to provide sufficient privacy protection while preserving enough precision for meaningful analytics, thus resolving the contradiction between exact computation and privacy protection.
Data Source
AI summary
Disclosed are techniques for determining data relationships between privacy-restricted datapoints, sourced over a computer network, which require data privacy measures concealing at least some datapoints from other clients in the network that the datapoint respectively do not originate from. A first client encrypts a first datapoint with a public key of a public/private encryption scheme and communicates it to the second client along with the public key. The second client encrypts a corresponding second datapoint with the public key, then determines a relationship between the two encrypted datapoints, and communicates the determined relationship to a central client along with the public key. Random noise is encrypted by the central client and added to the determined relationship, then sent together to the first client, followed by decryption by the first client using the private key. The central client extracts the random noise after receiving the decrypted determined relationship.