Secret Falsification Detection in Multi-Type Secret Sharing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional secret falsification detection techniques are limited to single-type secret sharing and cannot effectively detect falsification in secret computation using multiple types of secret sharing, which poses security risks.
Innovation Solution
A method for secret computation that involves generating random numbers and sharing them across multiple secret computation apparatuses to create randomized shared values, then performing secret computation and validation across different types of secret sharing, ensuring all computations are synchronized before validation to maintain security and detect falsification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional secret falsification detection technique is used, then falsification can be detected in single-type secret sharing, but it cannot detect falsification in multi-type secret sharing computation
Solution Approach 1:
The patent creates a universal validation framework that works across multiple secret sharing types (replicated secret sharing, linear secret sharing, Shamir secret sharing). The validation process uses a common checksum verification mechanism that adapts to different sharing schemes, making the falsification detection applicable to heterogeneous secret computation scenarios rather than being limited to a single type.
Solution Approach 2:
The patent changes the validation parameters by introducing type-specific random values (r0, r1, r2) corresponding to different secret sharing types. Each secret sharing type has its own validation equation with customized parameters (e.g., different multiplication factors like 2, 3, 4 for different sharing types), allowing the same validation framework to accommodate multiple sharing schemes by adjusting parameters rather than requiring separate validation mechanisms.
2Reliability
If randomized shared values are generated for each secret sharing type, then validation becomes possible, but computation complexity increases
Solution Approach 1:
The patent segments the validation process into distinct phases: randomization phase where random values are generated and paired with shared values to create randomized shared values, computation phase where secret computation is performed, and validation phase where checksums are verified. This segmentation allows each phase to be optimized independently and makes the overall complex process more manageable and implementable.
Solution Approach 2:
The patent introduces randomized shared values as an intermediary between the original shared values and the final computation results. These randomized shared values serve as a mediator that enables validation without exposing the actual secret values, allowing the system to verify computation correctness while maintaining security and concealability throughout the process.
3Stability of the object's composition
If all secret computation is completed before validation, then synchronization is achieved, but time for detection is delayed
Solution Approach 1:
The patent performs preliminary randomization of shared values before the actual secret computation takes place. By pre-generating randomized shared values and establishing the validation framework in advance, the system prepares the necessary components for later validation without delaying the main computation. The synchronization barrier ensures all computations are ready before validation begins, while the preliminary setup minimizes overall detection time.
Data Source
AI summary
Falsification is detected during secret computation that uses a plurality of types of secret sharing. A secret computation apparatus 1 uses shared values [a0], . . . , [aM-1] as inputs, and a function value [F([a0], . . . , [aM-1])] obtained with a function F for performing secret computation that uses J types of secret sharing as an output, and detects falsification during secret computation. A random number generating section 12 obtains shared values [r0], . . . , [rJ-1]. A randomizing section 13 multiplies the shared value [am] by the shared value [rj] to calculate a shared value [amrj], and generates a randomized shared value <am>:=<[am], [amrj]>. A secret computation section 14 obtains the function value [F([a0], . . . , [aM-1])] while including, in a checksum Cj, randomized shared values that are computation objects and randomized shared values that are computation results. A synchronizing section 15 keeps idling until all of secret computation that uses secret sharing are completed. A validating section 16 verifies that the shared value [φj] obtained by multiplying the sum of shared values [f0], . . . , [fμj-1] included in the checksum Cj by the shared value [rj] is equal to the shared value [ψj] obtained by adding shared values [f0rj], . . . , [fμj-1rj] included in the checksum Cj.