Device specific multiparty computation
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.
1. A method comprising:
generating channel impulse response (CIRs) of communication channels of the plurality of computing devices;
determining, using a Golay code received from a Golay code generator, a vector of device specific channel errors of a plurality of computing devices based on the CIRs of communication channels of the plurality of computing devices;
training a linear regression model using the vectors of the device specific channel errors from the plurality of computing devices 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, wherein the multi-party secret sharing protocol provides that the general access structure secret matrix cannot be constructed without shares from an authorized set of the computing devices.
2. The method of claim 1 , wherein the device specific channel errors are discrete gaussian errors and the method further comprising correcting the device gaussian errors using Golay codes.
3. The method of claim 1 , wherein generating the device specific channel errors further comprising generating device specific channel errors based on digitized version of the CIRs of communication channels of the plurality of computing devices and the Golay code, wherein the Golay code may be at least one of 24-bit extended binary code or a 23-bit perfect binary code.
4. The method of claim 1 , wherein training the linear regression model using the device specific channel errors further comprises training a learning with linear regression (LWLR) model.
5. The method of claim 1 , wherein generating the general access structure secret matrix using the LWE secrets further comprises generating the general access structure secret matrix that encodes the LWE secrets in a square matrix of a dimension equal to the number of computing devices in the authorized set of the computing devices.
6. The method of claim 1 , further comprising receiving a request to reconstruct the secret matrix and reconstructing the general access structure secret matrix using shares from the authorized set of the computing devices.
7. The method of claim 6 , wherein reconstructing the general access structure secret matrix using shares from the authorized set of the computing devices further comprising verifying the shares from each of the authorized set of computing devices.
8. One or more tangible computer-readable storage media encoding computer-executable instructions for executing a computer process, the computer process comprising:
generating channel impulse response (CIRs) of communication channels of the plurality of computing devices;
determining, using a Golay code received from a Golay code generator, a vector of device specific channel errors of a plurality of computing devices based on the CIRs of communication channels of the plurality of computing devices;
training a linear regression model using the vectors of the device specific channel errors from the plurality of computing devices 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, wherein the multi-party secret sharing protocol provides that the general access structure secret matrix cannot be constructed without shares from an authorized set of the computing devices.
9. The one or more tangible computer-readable storage media of claim 8 , wherein the device specific channel errors are discrete gaussian errors and the method further comprising correcting the device gaussian errors using Golay codes.
10. The one or more tangible computer-readable storage media of claim 8 , wherein generating the device specific channel errors further comprising generating errors based on digitized version of the CIRs of communication channels of the plurality of computing devices.
11. The one or more tangible computer-readable storage media of claim 8 , wherein training the linear regression model using the device specific channel errors further comprises training a learning with linear regression (LWLR) model.
12. The one or more tangible computer-readable storage media of claim 8 , wherein generating the general access structure secret matrix using the LWE secrets further comprises generating the general access structure secret matrix that encodes the LWE secrets in a square matrix of a dimension equal to the number of computing devices in the authorized set of the computing devices.
13. The one or more tangible computer-readable storage media of claim 10 , wherein the computer process further comprising receiving a request to reconstruct the general access structure secret matrix and reconstructing the general access structure secret matrix using shares from the authorized set of the computing devices.
14. The one or more tangible computer-readable storage media of 13 , wherein reconstructing the secret matrix using shares from the authorized set of the computing devices further comprising verifying the shares from each of the authorized set of computing devices.
15. In a computing environment, a system comprising:
memory;
one or more processors units;
a device specific multiparty computation system stored in the memory and executable by the one or more processor units, the multiparty computation attestation system encoding computer-executable instructions on the memory for executing on the one or more processor units, the computer process comprising:
generating channel impulse response (CIRs) of communication channels of the plurality of computing devices;
determining, using a Golay code received from a Golay code generator, a vector of device specific channel errors of a plurality of computing devices based on the CIRs of communication channels of the plurality of computing devices;
training a linear regression model using the vectors of the device specific channel errors from the plurality of computing devices to generate learning with error (LWE) secrets for each of the plurality of computing devices;
generating a secret matrix using the LWE secrets from each of the plurality of computing devices; and
distributing shares of the secret matrix to the plurality of computing devices based on a multi-party secret sharing protocol, wherein the multi-party secret sharing protocol provides that the secret matrix cannot be constructed without shares from an authorized set of the computing devices.
16. The system of claim 15 , wherein the device specific channel errors are discrete gaussian errors and the method further comprising correcting the device gaussian errors using Golay codes.
17. The system of claim 15 , wherein generating the device specific channel errors further comprising generating device specific channel errors based on digitized version of the CIRs of communication channels of the plurality of computing devices.
18. The system of claim 15 , wherein training the linear regression model using the device specific channel errors further comprises training a learning with linear regression (LWLR) model.
19. The system of claim 15 , wherein generating the secret matrix using the LWE secrets further comprises generating the secret matrix that encodes the LWE secrets in a square matrix of a dimension equal to the number of computing devices in the authorized set of the computing devices.
20. The system of claim 15 , wherein reconstructing the secret matrix using shares from the authorized set of the computing devices further comprising verifying the shares from each of the authorized set of computing devices.