US 12,289,399 B2
Device specific multiparty computation
Vipin Singh Sehrawat, Shugart (SG); Dmitriy Vassilyev, Longmont, CO (US); and Foo Yee Yeo, Shugart (SG)
Assigned to SEAGATE TECHNOLOGY LLC, Fremont, CA (US)
Filed by Seagate Technology LLC, Fremont, CA (US)
Filed on Oct. 29, 2021, as Appl. No. 17/514,755.
Prior Publication US 2023/0143175 A1, May 11, 2023
Int. Cl. H04L 9/08 (2006.01); G06F 17/18 (2006.01)
CPC H04L 9/085 (2013.01) [G06F 17/18 (2013.01); H04L 9/0875 (2013.01); H04L 2209/46 (2013.01)] 20 Claims
OG exemplary drawing
 
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.