Post-quantum secure and efficient outsourced machine learning

Cloud-assisted machine learning and analysis rely on cloud computing for various data processing services. Usually, data providers protect their data's privacy using encryption before outsourcing, but the resulting encrypted data are known to be of poor utility. To this end, existing works combine differential privacy with public key cryptography to support multiple user analyses and queries of outsourced data. However, with the emergence of quantum computing, the schemes mentioned above based on the traditional hardness assumption will be threatened. This paper proposes quantum-secure outsourcing of privacy-preserving data publishing schemes. In particular, we develop a highly efficient proxy re-encryption scheme based on the ring learning with errors problem. This scheme allows the cloud service providers to return only the encrypted data that satisfies the user's query without decrypting it, and the encrypted results can be decrypted using the user's key. Meanwhile, a novel noise addition mechanism is integrated into the proxy re-encryption scheme, enabling cloud service providers to achieve a dataset sanitization procedure that supports the requests from multiple data users and allows providers to go offline after uploading their datasets. Finally, we present a detailed theoretical analysis and report an experimental evaluation of the real dataset.