Homomorphic Bill Record Filtering for Private Amount Statistics
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Solution Overview
Problem
Existing privacy protection methods for personal bill records, such as symmetric encryption, require decryption for computations, weakening privacy protection, and lack support for direct computations on ciphertext, especially in scenarios like personal bill filtering and amount statistics.
Innovation Solution
A privacy-preserving method using homomorphic encryption, specifically the Paillier encryption system, to encrypt and process personal bill data, allowing filtering and statistics on ciphertext without decryption, supporting AND/OR connections for filtering conditions and enabling encrypted amount statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If symmetric encryption algorithms (AES) are used to encrypt personal bill data, then encryption efficiency and maturity are improved, but the ability to perform computations on ciphertext is lost, requiring decryption first which weakens privacy protection
Solution Approach 1:
The patent replaces symmetric encryption (AES) with homomorphic encryption (Paillier scheme) to substitute the mechanical decryption-computation-encryption process with a direct ciphertext-computation process. This substitution enables computations on encrypted data without decryption, resolving the contradiction between encryption maturity and privacy protection effectiveness.
Solution Approach 2:
The patent changes the encryption parameter from symmetric key-based AES to asymmetric Paillier homomorphic encryption. This parameter change enables the ciphertext to retain computational properties while maintaining security, allowing both efficient encryption and direct computation on encrypted data without compromising privacy.
2Reliability
If homomorphic encryption is used to perform computations on ciphertext, then privacy protection is improved, but the complexity of cryptographic operations and algorithm design increases
Solution Approach 1:
The patent segments the complex homomorphic encryption process into manageable components: key generation (generate public/private key pairs), encryption (convert plaintext to ciphertext), homomorphic operations (perform computations on ciphertext), and decryption (convert ciphertext to plaintext). This segmentation reduces the complexity burden on any single operation while maintaining overall privacy protection.
Solution Approach 2:
The patent introduces a trusted third party as an intermediary that handles complex cryptographic operations. This intermediary manages the key pairs and facilitates homomorphic computations, reducing the complexity burden on the user and simplifying the overall system architecture while maintaining strong privacy protection.
3Reliability
If data is encrypted before upload to server, then privacy protection is improved, but the ability to perform filtering and statistics operations is lost without decryption
Solution Approach 1:
The patent makes the encrypted data structure universal by designing it to support multiple operations: filtering by amount range, time range, and keyword search, as well as statistical operations like summing amounts. The homomorphic encryption scheme enables all these operations to be performed directly on ciphertext, providing multi-functionality without requiring decryption and thus maintaining both security and versatility.
Solution Approach 2:
The patent creates a computational copy of the data in encrypted form. The homomorphic encryption produces a ciphertext that preserves the structural and computational properties of the original plaintext, allowing filtering and statistics operations to be performed on this encrypted copy without accessing the actual data values, thus maintaining privacy while enabling versatile processing.
Data Source
AI summary
Provided is a privacy-preserving method for personal bill record filtering and amount statistics. A user homomorphically encrypts data involved in personal bills, including bill amount, income-expenditure category, bill description, and occurrence time, and uploads ciphertext data to a server. Then the user inputs three conditions: keywords for the bill description, an interval for the bill amount, and an occurrence time range. The server, according to an AND/OR combination of the three conditions, performs a combination of homomorphic operations on the ciphertext data, and returns all results to the user, and the user performs decryption to recover query results. Finally, the user inputs a required income-expenditure category and a required occurrence time range. The server performs homomorphic operations and returns operation results to the user. The user performs homomorphic decryption to calculate a total amount, thereby completing amount statistics and generating a final query result.