Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata
This addresses privacy concerns for patients and healthcare providers in telemedicine, though it appears incremental as it builds on existing NFA and encryption techniques.
The paper tackles the problem of privacy leakage in remote medical treatment by proposing P-Med, a system that uses encrypted nondeterministic finite automata (NFA) to represent medical models and enable patient-centric diagnosis and treatment recommendations, achieving confidentiality for illness states and results without specifying concrete performance numbers.
In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency.