🎯Privacy-Preserving Computation Solution
In this solution, developers could empower multiple party privacy-preserving computation on encrypted data.
The How:
Register and create your own Data Cocoon (A secure data mat for you only)
Write/Upload: The user's data (Alice) is encrypted on the client side, then written or uploaded to Mind Network with encryption.
Compute: The encrypted data from the data owner (Alice) could be configured to allow other dapps to perform encrypted query or computation on the data. During query or computation, the data can be still encrypted, even in the memory, which ensures end-to-end encryption.
This way, the computation result is only accessible to the designated access control, which makes the data available but invisible to computation participants.
The What:
The value from multi-party computation on encrypted data is unique, which unleashes a number of use cases to bring the protection of on-chain data privacy and security to the next level.
The exchange platforms, whether centralized or decentralized, can leverage Mind Network to execute the order books without knowing the investor's position
For lending platforms, credit evaluation could be performed through Mind Network without exposing users' financial records.
For medical research, personal genetic data could be persisted and shared for computation from medical institutions without sharing the entire genetic data.
For legal platforms, the legal documents can be protected by Mind Network and shared with lawyers to make sure no other ones have access, etc.
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