LAUSR

This letter proposes a novel privacy-aware “blind” cloud infrastructure to be utilized for storage, processing, and organization of health data. Traditional healthcare systems rely on cloud computing servers for back-end storage and processing. However, cloud servers are heavily vulnerable to privacy threats and the problem is even more intense as physiological data carry sensitive information. To resolve the aforementioned issue, this letter proposes the blind cloud framework. The goal is to take advantage of the enormous computing and storage abilities of the cloud servers, and yet maintain data anonymity simultaneously. To preserve the privacy of the medical data, the cloud server is forcefully blinded, i.e., the identities of the patients are masked off and a pseudo-identity is generated, thereby, obtaining unidentified in-cloud data for storage and analysis. We also propose a parallel method to be executed within the non-cloud servers for efficient and lossless identity management and retrieval. Results indicate that the performance of the processes of pseudo-identity generation and identity retrieval is independent of the data volumes, and negligibly vary with the increase in the number of the clients of the system.