Secret sharing via blockchain distribution
Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.
1. A memory device comprising a hardware processor and the memory device storing instructions that when executed cause the hardware processor to perform operations, the operations comprising:
receiving local update generated by a federated learning model executed by a mobile device;
splitting the local update into multiple shares via a secret sharing algorithm;
generating cryptographic hash values by hashing the multiple shares using a cryptographic hashing algorithm;
determining a number N B of different blockchains for a distribution of the cryptographic hash values, the number N B of the different blockchains based on a number N R of recipients of the different blockchains according to a ratio of N R /N B ;
distributing the cryptographic hash values via a master blockchain of the different blockchains dedicated to the mobile device and via a sub-blockchain of the different blockchains dedicated to the federated learning model;
retrieving verification hash values generated by the hashing of current versions of the local update associated with the federated learning model using the cryptographic hashing algorithm;
comparing the cryptographic hash values distributed via the different blockchains to the verification hash values generated by the hashing of the current versions of the local updates; and
verifying that the current versions of the local updates are authentic in response to the verification hash values satisfying the cryptographic hash values distributed via the different blockchains.
2. The memory device of claim 1 , wherein the operations further comprise determining that the current versions of the local updates are inauthentic in response to the verification hash values failing to satisfy the cryptographic hash values.
3. The memory device of claim 1 , wherein the operations further comprise receiving a noun identifier that identifies the federated learning model.
4. The memory device of claim 1 , wherein the operations further comprise receiving a noun identifier that identifies the mobile device.
5. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by a mapping software application as the federated learning model.
6. The memory device of claim 1 , wherein the operations further comprise generating a suggestion.
7. The memory device of claim 1 , wherein the operations further comprise generating a result.
8. The memory device of claim 1 , wherein the operations further comprise generating a modification.
9. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by a dating software application as the federated learning model.
10. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by a driving software application as the federated learning model.
11. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by an energy software application as the federated learning model.
12. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by a predictive software application as the federated learning model.
13. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by a social networking software application as the federated learning model.
14. The memory device of claim 1 , wherein the operations further comprise receiving the local update generated by an artificial intelligence software application as the federated learning model.