Data privacy and Decentralized Artificial Intelligence (DeAI). PART 1
The concept of decentralized artificial intelligence (DeAI) is a new paradigm that resolves problems related to data privacy of centralized AI systems. This paper discusses the data protection systems, blockchain integrations, and possible uses by DeAI. Conventional AI solutions are based on large volumes of data stored in centralized servers, which puts the privacy of users at risk of data breaches. DeAI, however, is based on distributed technologies such as blockchain to restore ownership of data to the user, improving transparency and security.Federated learning and homomorphic encryption are the approaches underlying the principles of DeAI. Federated learning allows local processing of data on computers, with model updates only being sent to a central server, and raw data is not sent. Such method is especially useful in healthcare where AI models can be trained with using patient data without any violations of privacy. An example is DeCaPH framework, which supports collaborative learning within a multi-hospital environment, which guarantees privacy-sensitive partnerships. By using blockchain as a part of DeAI, smart contracts can control access to data; when the user consents to share data, the operation is registered as permanent. This builds transparency in data markets and avoids privacy threats.In practice, DeAI privacy-conscious AI agents act as blockchain custodians. Such guardians are a combination of lightweight AI models and blockchain protecting the information of users.
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