Sammy Kerata Oina
997fb3bf48
* Add hash verification for datasets and algorithms Enhanced data integrity checking by incorporating hash fields in Dataset and Algorithm structures, and modified the corresponding service logic to validate these hashes during processing. The update includes SHA-3 for hash computation, replacing the former SHA-256 usage, and ensures the provided data matches the expected hash from the manifest to prevent processing malformed or corrupted data. - Introduce `Hash` field to both Dataset and Algorithm structs to store the expected hash value. - Implement SHA-3 hashing within service methods that process the data, ensuring consistency with newly added `Hash` fields. - Add error handling for hash mismatches, preventing further processing and alerting to potential data integrity issues. - Update Protocol Buffers serialization to accommodate the new hash fields for gRPC communication. - Modify manager service's Run method to pass the hash information when creating agent configurations. Go module dependencies were updated to include the new SHA-3 package and upgrade Go version to 1.21.6 for improved stability and compatibility. Signed-off-by: SammyOina <sammyoina@gmail.com> * Remove identifiers from protobuf and related code The protobuf definitions and related service handling code have been revised to drop specific identifier fields (`AlgorithmID` and `DatasetID`) to simplify API responses and internal function signatures. These removals streamline the overall data flow between components, reduce unnecessary data transmission, and lead to an aligned server-client expectation where identifiers are no longer a part of the response payload. Consequently, these changes simplify the logic within various functions and client commands, reinforcing encapsulation by ensuring that internal identifiers do not need to be managed or exposed unnecessarily. Signed-off-by: SammyOina <sammyoina@gmail.com> * fix lint Signed-off-by: SammyOina <sammyoina@gmail.com> --------- Signed-off-by: SammyOina <sammyoina@gmail.com>
Cocos AI
Cocos AI (Confdential Computing System for AI/ML) is a platform for secure multiparty computation (SMPC) based on the Confidential Computing and Trusted Execution Environments (TEEs).
With Cocos AI it becomes possible to run AI/ML workloads on combined datasets from multiple organizations while guaranteeing the privacy and security of the data and the algorithms. Data is always encrypted, protected by hardware secure enclaves (Trusted Execution Environments), attested via secure remote attestation protocols, and invisible to cloud processors or any other 3rd party to which computation is offloaded.
Usage
The quickest way to start using Cocos is via the CLI. The latest version can be downloaded from the official releases page.
It can also be built and used from the project's root directory:
make cli
./build/cocos-cli version
Additional details on using the CLI can be found in the CLI documentation.
Documentation
Official documentation is hosted at [Cocos official docs page][docs]. Documentation is auto-generated, checkout the instructions on official docs repository.
License
Cocos AI is a proprietary product created by Ultraviolet company.