Fully Homomorphic Encryption: The Holy Grail of Privacy Protection in Artificial Intelligence?

Fully Homomorphic Encryption ( FHE ), as an important breakthrough in the field of encryption, is bringing new possibilities for privacy protection in artificial intelligence applications. In today's digital age, we enjoy the convenience brought by personalized recommendation services on one hand, but on the other hand, we are increasingly worried about privacy breaches. FHE may be able to resolve this contradiction, allowing us to enjoy customized services without sacrificing privacy.

The rise of Artificial Intelligence as a Service ( AIaaS ) has enabled ordinary users to access advanced neural network models. However, current AIaaS sacrifices user privacy while providing convenience, as servers can access users' input data. With the introduction of privacy protection regulations such as GDPR, we urgently need to develop robust privacy protection mechanisms within the AIaaS process.

FHE provides a solution for data privacy issues in cloud computing. It supports operations such as addition and multiplication on ciphertext, allowing the server to perform computations on encrypted data without decrypting it. In deep neural networks based on FHE, users only need to transmit the encrypted input data to the cloud server, which performs homomorphic computations on the ciphertext and returns the encrypted output, keeping the user's data in an encrypted state throughout the entire process.

FHE has broad application prospects in various fields such as advertising, healthcare, data mining, and finance. However, FHE still faces some limitations, such as complex multi-user support, huge computational overhead, and limited supported operations. Nevertheless, some companies like Zama, Privasee, Octra, and Mind Network have already begun exploring the application of FHE in the fields of artificial intelligence and cryptocurrency.

Although FHE still faces many challenges in practical applications, with the continuous advancement of algorithms and hardware, it is expected to bring revolutionary changes to the field of artificial intelligence, achieving a balance between privacy protection and efficient computation. In the future, FHE may become a key technology for building secure and privacy-protecting AI services.