# Data Privacy

**Miners’s asset**

* Every miner (also referred to as a model host) within the network generates a distinct pair of public and private keys.
* Miners disclose their public keys for user access.

**Data Encryption by Users**

* Users create a symmetrical encryption key for the secure encryption of input data destined for machine learning model inference.
* This symmetrical key is then encrypted using the public key of each selected miner. Should a user choose N miners, this encryption process is replicated N times with each miner's public key.
* The user compiles the encrypted input data, the assortment of encrypted symmetrical keys, and their public key into a submission. This submission is then broadcasted across the network.

**Data Handling by Miners**

* Upon receiving a user's submission, a miner decrypts the symmetrical key using its private key.
* With the symmetrical key, the miner decrypts the user's original input data.
* The miner processes the model inference task with the decrypted input data.
* Once the task is complete, the miner encrypts the result using the user's public key.
* This encrypted output is shared on the network, accessible solely by the originating user, who can decrypt it with their private key.

**Security Measures**

* The implementation of symmetrical encryption for input data ensures exclusive access to the original data by the corresponding miner via its private key.
* Output data encrypted with the user's public key guarantees that only the user can decrypt and access the model's results.
* This layered encryption strategy effectively shields user data from unauthorized viewing at all stages.
* The public key infrastructure (PKI) framework confirms that encrypted messages are decipherable solely by the designated recipients, thus securing end-to-end data confidentiality.