Secure Search Vector Decomposition for Multi-Result Encrypted Queries
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Solution Overview
Problem
Existing secure computation methods cannot compute confidential values of multiple pieces of data satisfying predetermined search conditions from a sequence of aligned data and a query.
Innovation Solution
A secure search system comprising three or more secure search devices that utilize vector decomposition, first and second detection means, and output computation to compute a share of a vector containing elements satisfying search conditions from aligned data and a query, using secure distribution methods for concealment, addition, subtraction, multiplication, division, and comparison operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If binary search is used in secure computation to find confidential values equal to or larger than a query, then the search can be performed on encrypted data, but only a single confidential value can be computed, not multiple values satisfying predetermined search conditions
Solution Approach 1:
The invention divides the search process into multiple stages by segmenting the result vector into multiple sub-vectors. Each sub-vector corresponds to a specific range of sorted data, and the system iteratively searches through these segments to find all values satisfying the query condition. This segmentation enables the system to return multiple results while maintaining secure computation properties.
Solution Approach 2:
The invention transitions from a one-dimensional binary search (returning a single value) to a multi-dimensional approach by creating a result vector with multiple elements, each representing a different segment of the sorted data. This dimensional expansion allows the system to simultaneously represent and process multiple potential results while maintaining encryption.
2Reliability
If encryption is applied to data and computation is performed on encrypted values, then data security is maintained, but the complexity of computation increases compared to plaintext processing
Solution Approach 1:
The system performs preliminary actions by pre-sorting the encrypted data into a sorted order before the search operation. This pre-processing enables the use of efficient binary search algorithms on the encrypted data, reducing the computational complexity during the actual search phase while maintaining security.
Solution Approach 2:
The invention introduces an intermediary result vector that serves as a mediator between the encrypted data and the final search results. This intermediate structure allows the system to perform comparisons and selections on encrypted values without needing to decrypt them, thereby maintaining security while managing computation complexity through structured processing.
Data Source
AI summary
Provided is a technique for computing confidential values of a first plurality of pieces of data satisfying predetermined search conditions from a sequence of confidential values of N pieces of aligned data and confidential values of a query. A vector decomposition means for computing a share [[→vi]] of a vector →vi (i=1, . . . α) having a length β satisfying predetermined conditions from a share of an aligned vector →v having a length N, a first detection means for computing a share of a vector [[→f]] having a length α satisfying predetermined conditions from the share [[→vi]], a partial vector computation means for computing a share [[→a]] of a vector →a having a length βγ satisfying predetermined conditions using the share [[→vi]] and the share [[→f]], a second detection means for computing a share of [[→b]] a vector →b having a length β satisfying predetermined conditions from the share [[→a]], and an output computation means for computing a share of a search result vector from the share [[→a]] using the share [[→b]].