Achieving privacy-preserving sensitive attributes for large universe based on private set intersection

Nowadays, an increasing amount of data has been sent to the cloud for analysis and storage, and data security in the cloud has been widely concerned. Among them, CP-ABE is regarded as one of the most promising technologies to protect outsourced data. However, in most CP-ABE schemes, attackers may obtain user privacy information from policy plaintext. More likely, partial policy hiding programme neither satisfies full policy hiding nor applies to the needs of the large universe. In this paper,we hide both attribute values and names in policy by using private set intersection (PSI). The CP-ABE programme not only supports a complete hiding policy, but also can calculate the authorization relationship and mapping relationship between user passwords and keys. We use a polynomial-based PSI and a recursive algorithm to calculate the authorization relationship. And with the help of the algorithm and the label vector, the mapping is determined under communication restrictions. Through the outsourcing index, we achieve an efficient hiding strategy and effectively reduce the user's computing overhead. Finally, theoretical analysis and experiments show that our model has better performance while effectively protecting sensitive attributes.