Homomorphic Encryption for Secure Joint Statistical Analysis
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Solution Overview
Problem
In business data processing, data leakage often occurs when multiple parties attempt to jointly calculate statistics, as existing protocols lack sufficient security, allowing sensitive information to be compromised.
Innovation Solution
A data processing method and apparatus utilizing homomorphic encryption, where identification data is encrypted and used to perform calculations on business data ciphertext, ensuring that only encrypted results are shared between parties, preventing data leakage by maintaining confidentiality of individual datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple parties jointly calculate statistics using existing protocols, then statistical analysis can be completed, but data leakage occurs compromising security
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that enables statistical calculations on encrypted data without decryption. The encryption scheme acts as a mediator between the need for data analysis and the requirement for data security, allowing joint statistical computation while maintaining confidentiality of individual datasets throughout the process
Solution Approach 2:
The patent transforms the state of data from plaintext to ciphertext using homomorphic encryption, changing the parameter of data visibility while preserving computational functionality. This parameter change allows the data to remain secure (in encrypted form) while still enabling statistical operations to be performed on the encrypted values
2Reliability
If data is encrypted to prevent leakage, then security is improved, but calculation complexity increases
Solution Approach 1:
The patent replaces traditional decryption-then-calculate mechanics with homomorphic encryption mechanics that allow direct calculation on encrypted data. This substitution eliminates the need to decrypt data before performing statistical operations, reducing the complexity steps while maintaining security throughout the calculation process
3Measurement precision
If plaintext data is shared for joint calculation, then statistical accuracy is improved, but data exposure risk increases
Solution Approach 1:
Homomorphic encryption serves as an intermediary that enables accurate statistical calculations to be performed on encrypted data without exposing the underlying plaintext. The encryption mechanism mediates between the need for statistical precision and the need to prevent data exposure, allowing both goals to be achieved simultaneously
Data Source
AI summary
A computer-implemented method includes: encrypting, by a first data party, identification data to generate a first identification data ciphertext, in which the first data party holds an identification dataset including the identification data; sending a first ciphertext set to a second data party, in which the first ciphertext set includes the first identification data ciphertext; receiving a second ciphertext set from the second data party; decrypting the second identification data ciphertext to generate a third identification data ciphertext, in which a third ciphertext set includes the third identification data ciphertext; receiving a fourth ciphertext set from the second data party, obtaining the third identification data ciphertext common to the third ciphertext set and the fourth ciphertext set; calculating a homomorphic addition result of the business data ciphertext corresponding to the third identification data ciphertext; and sending the homomorphic addition result to the second data party.