Secret Sharing Multi-Party Computation Device with Dynamic Share Updating
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Solution Overview
Problem
Current secret sharing-based multi-party computation systems face increased security threats due to sophisticated data leakage attacks, necessitating enhanced security measures to protect data integrity and privacy.
Innovation Solution
A device and method for secret sharing-based multi-party computation that involves generating and updating shares using random numbers to cancel out their influence, ensuring that original data can only be restored by combining shares from multiple devices, thereby enhancing security and traceability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If secret sharing-based multi-party computation is used to protect data privacy, then data security against leakage is improved, but the system becomes more vulnerable to sophisticated large-scale attacks requiring further security enhancements
Solution Approach 1:
The patent divides the secret data into multiple shares and distributes them to different devices. Each device holds only a portion of the data, and the original secret can only be reconstructed when a threshold number of shares are combined. This segmentation prevents any single device from having access to the complete secret, thereby resisting sophisticated attacks targeting individual components.
Solution Approach 2:
The patent implements dynamic share updating mechanisms where shares are periodically refreshed using random values. This dynamic transformation ensures that even if an attacker compromises a device, the stolen shares become invalid after updates, providing evolving security that adapts to and counters sophisticated large-scale attacks.
2Adaptability or versatility
If data is provided to third parties for utilization, then data availability and economic value are improved, but privacy protection strength deteriorates due to pseudonymization and anonymization trade-offs
Solution Approach 1:
The patent introduces secret sharing as an intermediary mechanism between data providers and third parties. Instead of directly sharing sensitive data, the system distributes encrypted shares to multiple devices, allowing third parties to perform computations on the shared data without accessing the underlying sensitive information. This enables data utilization while maintaining strong privacy protection, eliminating the need for pseudonymization or anonymization trade-offs.
Data Source
AI summary
A device participates in secret sharing-based MPC. Original data can be restored by combining a share of the device with a corresponding share of another device. The device includes means for acquiring random number and means for updating a share of the device on the basis of the acquired random number. A method for updating by the updating means is designed to perform update in a manner that a share of the device updated on the basis of the acquired random number is combined with the corresponding share of the other device updated on the basis of the random number to cancel an influence of the random number and restore the original data.