PQHealthcare: An efficient cloud data sharing system via Functional Encryption from lattices

With the rapid development of the large language models ($\mathrm{LLM}$) service on the cloud, especially in the healthcare sector, the usage of large models is becoming increasingly popular. However, training these models always involves sensitive information containing lots of personal medical data. And thus, such training processes might result in the exposure of privacy information. In order to help the users eliminate their concerns and share their data in a secure way, we need to find a privacy-preserving method for data sharing in the public cloud service environment.

While traditional public-key encryption ($\mathrm{PKE}$) schemes can effectively encrypt healthcare data, they typically offer protection in an “all-or-nothing” manner, lacking flexibility and imposing a significant computational burden on public cloud servers. In contrast, Functional Encryption ($\mathrm{FE}$) offers a more flexible way of encryption with access control, making it ideal for the cloud data sharing environment. Furthermore, we observe that the majority of computations involved in training large models can be represented by inner product functions. To establish a secure public cloud data sharing system, we propose an efficient $\mathrm{FE}$ scheme for inner product function class, making it well-suited for various real-world applications. We will demonstrate that our scheme achieves post-quantum security based on lattice assumptions.