Device-Specific MPC Using LWE Secrets from Channel Errors
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Solution Overview
Problem
Secure multiparty computation (MPC) faces challenges such as high communication complexity, costly input verification, lack of post-quantum security, and limited support for general access structures in efficient MPC schemes.
Innovation Solution
A multi-party secret sharing protocol that is device-specific, where a secret matrix is tied to individual computing devices. This involves determining device channel errors based on channel impulse response, training a linear regression model to generate Learning With Error (LWE) secrets, and distributing shares of a general access structure secret matrix to devices, ensuring the matrix cannot be constructed without shares from an authorized set of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MPC protocols are used, then security is provided, but communication complexity increases and efficiency decreases
Solution Approach 1:
The secret is segmented into multiple shares distributed to different devices. Each device holds a portion of the secret, and the secret can only be reconstructed when a sufficient number of shares are combined. This segmentation reduces the communication burden on any single device while maintaining security through distributed secret sharing.
Solution Approach 2:
A trusted dealer acts as an intermediary to distribute secret shares to participating devices. The dealer generates the secret shares and distributes them according to the access structure, eliminating the need for direct complex communication between all pairs of devices. This intermediary approach simplifies the communication topology while preserving security guarantees.
2Productivity
If efficient MPC schemes are used, then communication complexity is reduced, but post-quantum security is lost
Solution Approach 1:
The system changes the cryptographic parameters by using lattice-based cryptography (Learning With Errors problem) instead of traditional cryptographic assumptions. This parameter change enables post-quantum security while maintaining reasonable efficiency, as the security relies on the hardness of lattice problems which are believed to be resistant to quantum attacks.
Solution Approach 2:
The patent replaces traditional cryptographic mechanisms with lattice-based cryptographic mechanisms. Instead of relying on factoring or discrete logarithm problems, the system uses the hardness of finding short vectors in lattices, providing post-quantum security while maintaining computational efficiency through optimized lattice operations.
3Ease of operation
If standard secret sharing is used, then simplicity is maintained, but general access structures are not supported
Solution Approach 1:
The secret sharing scheme is designed to be universal by supporting general access structures through the use of monotone span programs. The same basic secret sharing mechanism can accommodate different access structures (threshold, hierarchical, arbitrary authorized sets) by changing the span program definition, providing both simplicity in implementation and flexibility in access control policies.
4Adaptability or versatility
If device-independent MPC is used, then versatility is achieved, but device-specific security guarantees are lost
Solution Approach 1:
The patent implements device-specific secret sharing where each device receives shares tailored to its specific identity and security requirements. The secret sharing scheme incorporates device-specific parameters and identifiers, allowing each device to have customized security guarantees while still participating in the overall MPC protocol. This local quality approach enables both device independence for participation and device-specific security for protection.
Data Source
AI summary
The disclosure provides systems and methods for a multi-party secret sharing protocol that includes determining device channel errors of a plurality of computing devices based on channel impulse response (CIR) of communication channels of the plurality of computing devices, training a linear regression model using the device channel errors to generate learning with error (LWE) secrets for each of the plurality of computing devices, generating a general access structure secret matrix using the LWE secrets from each of the plurality of computing devices, and distributing shares of the general access structure secret matrix to the plurality of computing devices based on a multi-party secret sharing protocol.