Building Privacy Preserving RAG with Homomorphic Encryption (www.subhashdasyam.com)

A recent development in AI privacy pertains to the implementation of homomorphic encryption in Retrieval-Augmented Generation (RAG) systems, particularly within healthcare settings. Traditional methods of handling vector embeddings — dense numerical representations of text or data — have demonstrated vulnerabilities, allowing attackers to reconstruct sensitive information from seemingly anonymized data. This poses significant risks, especially in environments governed by strict compliance laws like GDPR and HIPAA. Homomorphic encryption offers a solution by enabling computations on encrypted data, thereby eliminating exposure during processing. The proposed system employs the Paillier cryptosystem, which allows for specific mathematical operations on encrypted data, facilitating the calculation of cosine similarity without decrypting sensitive embeddings. Though there are trade-offs in terms of storage and computational efficiency, the security benefits are substantial. The architecture leverages PostgreSQL for storage, conducting batch operations efficiently, while employing optimizations for faster data processing. This innovation represents a critical step towards enhancing the privacy and security of AI applications, particularly in sectors managing highly sensitive information.