Data Privacy and Transparency

Dil Network realizes data privacy and transparency through a combination of the non - tamperable feature of blockchain, federated learning, and homomorphic encryption.

• Blockchain's Non - Tamperable Feature: Blockchain serves as the foundation for data storage and management in Dil Network. Its non - tamperable nature ensures that once data is recorded on the blockchain, it cannot be altered without the consensus of the majority of nodes in the network. This property provides a high level of data integrity, which is crucial for transparency. For example, in the process of AI - generated content storage, every piece of content and its associated metadata are stored on the blockchain. Any attempt to modify this information will be detected, making it possible to trace back the origin and changes of the data, thus enhancing transparency. At the same time, it also prevents malicious data tampering, protecting the privacy of data sources.

• Federated Learning: Federated learning is a key technology adopted by Dil Network to protect data privacy. In traditional machine - learning models, data needs to be centralized for training, which poses a significant risk to data privacy. Federated learning, however, enables multiple parties (such as different nodes in the network) to jointly train a machine - learning model without sharing their raw data. Each node trains the model using its local data, and only the model parameters are exchanged. For instance, in a scenario where multiple hospitals want to collaborate on an AI - based disease prediction model, they can use federated learning in Dil Network. Each hospital can train the model with its patient data while keeping the data within its own premise. This way, patient data privacy is strictly protected, and at the same time, the collaborative training of the model is achieved, promoting data - driven AI development in a privacy - preserving manner.

• Homomorphic Encryption: Dil Network also utilizes homomorphic encryption to further enhance data privacy during the computing process. Homomorphic encryption allows computations to be performed on encrypted data without decrypting it first. When AI algorithms need to process user - related data, the data can be encrypted using homomorphic encryption techniques. The encrypted data is then processed by the AI model, and the result is also in an encrypted form. Only the authorized parties with the decryption key can obtain the final decrypted result. This ensures that at no point during the data processing flow is the sensitive data exposed in plaintext, providing a high level of data privacy protection. For example, in a financial application where AI is used to analyze user transaction data for fraud detection, homomorphic encryption can be applied to the transaction data. The AI model can analyze the encrypted data and return encrypted fraud - detection results, protecting the privacy of users' financial transactions while still enabling useful data analysis.