Asymptotically Stable Privacy Protection Technique for fMRI Shared Data over Distributed Computer Networks

Abstract

This paper presents a computational technique that leverages the asymptotic-stabilization behavior of transition probabilities that are characterized by two-state Markov chain. These asymptotic probabilities help the computational technique to protect the privacy of the functional magnetic resonance imaging (fMRI) data that is shared over a public distributed computer network. In general, the fMRI signals reveal a large number of correlated brain features that can be utilized in the development of predictive models for extracting brain networks and infer privacy information of an individual. These features make fMRI data highly vulnerable to privacy attacks. To conceal these features for privacy protection, we transform them to an asymptotic state of an fMRI signal using the concepts of asymptotic stabilization with two-sate Markov chain, and the compressed sensing and compressed learning techniques. The proposed predictive model is built using the asymptotically stabilized fMRI signals, rather than the original signals, which enhance the protection of privacy. Hence, the transformed signal, instead of the original signal, may be shared in public computer networks, such as the cloud computing network. The computer simulations show that the proposed predictive model provides very high prediction accuracy, while providing very strong privacy protection.