Batch Private Information Retrieval Using Cuckoo Hashing and Constant Weight Codes
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Solution Overview
Problem
Existing batch private information retrieval methods face high computational and communication overhead, and are not practical for privacy-sensitive scenarios due to inefficient use of homomorphic encryption and additional communication costs.
Innovation Solution
A method combining cuckoo hash technique, constant weight code encoding, and homomorphic encryption, optimized with Single Instruction Multiple Data (SIMD) technique, to reduce both computational and communication overhead, enabling efficient batch queries while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional word private information retrieval method is used, then query privacy is protected, but processing efficiency is low due to repeated execution for multiple queries
Solution Approach 1:
The patent combines multiple single queries into a single batch query operation. By merging multiple query requests into one unified cryptographic operation, the system processes multiple items simultaneously rather than executing them sequentially, thereby improving productivity and reducing the time loss associated with repeated executions.
2Device complexity
If SealPIR batch query method is used, then computational overhead is reduced, but communication overhead increases due to common encoding properties
Solution Approach 1:
The patent extracts and eliminates the redundant common encoding portion from batch queries. By separating the common encoding that is identical across multiple queries from the unique query-specific portions, the system transmits only the necessary differential information, thereby reducing communication overhead while maintaining the computational efficiency benefits of batch processing.
3Quantity of substance
If constant weight code encoding is applied to batch queries, then communication overhead is reduced, but computational complexity increases without optimized homomorphic encryption usage
Solution Approach 1:
The patent optimizes the parameters of homomorphic encryption to better suit constant weight code encoding. By adjusting encryption parameters such as polynomial degree, modulus, and key dimensions, the system achieves a balance where communication volume is reduced through efficient encoding while computational complexity remains manageable through optimized cryptographic parameters.
Solution Approach 2:
The patent performs preliminary optimization of homomorphic encryption schemes before applying constant weight code encoding to batch queries. By pre-configuring the encryption parameters and selecting appropriate cryptographic primitives in advance, the system prepares the computational framework to efficiently handle the encoding process, thereby reducing the actual computational complexity during query execution.
4Reliability
If batch private information retrieval is implemented without optimization, then privacy is protected, but practical applicability is limited due to high overhead
Solution Approach 1:
The patent segments the batch query process into distinct modular components: hash function generation, constant weight code encoding, homomorphic encryption, and result decryption. This segmentation allows each component to be independently optimized and tuned, making the overall system more practical for real-world deployment while maintaining privacy protection through the coordinated operation of these modular segments.
Data Source
AI summary
A batch private information retrieval method and apparatus, including: agreeing on a hash function, calculating a serial number index in an original database, retaining a result of each serial number index for each hash function, and obtaining a first hash bucket; filling an index of a batch query into a second hash bucket of a same length as the first hash bucket by the client, and encoding a filling result by using a constant weight code encoding manner; encrypting an obtained encoding result as a batch query request by the client; comparing the batch query request with the first hash bucket by the server to obtain a corresponding intermediate result, calculating correspondingly the intermediate result and the original database to obtain a corresponding query result ciphertext; and decrypting the query result ciphertext by the client, and thus simultaneously obtaining a query result of the batch query request.