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At the vanguard of healthcare research, many promising new methods rely on large-scale access to patient data. Given the ethical, regulatory, organizational and technical barriers associated with this sort of research, there is a pressing need for software platforms that address privacy and accuracy concerns. To date, a lack of such solutions has meant healthcare researchers have faced a degree of data scarcity.

A desire to address this problem motivated the research led by Shadan Ghaffaripour, a doctoral candidate in Ryerson鈥檚 Department of Computer Science.

鈥淯tilizing the infrastructure that blockchain provides, our platform enables researchers to solicit contributions to a well-defined research study from a large crowd of volunteers,鈥� says Ghaffaripour. 鈥淥ur method enables what is effectively a one-on-one collaboration between researchers and patients who would like to volunteer to participate in a study.鈥�
Ghaffaripour鈥檚 research findings were presented at the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) in a paper titled which she co-authored with her supervisor, Dr. Ali Miri.

鈥淓stablishing mutual trust between participants and researchers is pivotal,鈥� says Ghaffaripour. 鈥淲ith crowdsourcing technology, the main challenge is providing confidentiality assurance to the public while assuring researchers of the veracity of patient contributions.鈥�

To achieve this, the pair employed a cryptographic primitive called Zero-knowledge Argument of Knowledge (zk-SNARK), which is a form of zero-knowledge proof. These protocols can verify a statement without conveying any information apart from the fact that the statement is true. This allows participants to make contributions without exposing their privacy-sensitive health data while enabling researchers to efficiently verify the validity of contributions.

This contribution is novel in the field of privacy-preserving computation research. 鈥淥ur main contribution is leveraging specialized cryptographic tools (zero-knowledge proofs) and blockchain technology for medical research purposes,鈥� says Ghaffaripour. 鈥淭o the best of our knowledge, this has not been done before.鈥�

The application also takes into account that researchers often incentivize participants with a monetary reward. It incorporates a blockchain method known as 鈥渟mart contracts鈥� that ensures a fair reciprocal exchange of data for reward without the need for involvement of an intermediary.

This new method facilitates data-driven research, expedites the process of finding and accessing data, and accelerates machine learning of a research model through access to high-quality data. From a patient鈥檚 point of view, the model offers necessary transparency to build trust in the system and incentivizes study participation while not compromising privacy.

Together, these benefits achieve a more fundamental objective. 鈥淯ltimately, our goal is to accelerate the discovery of new treatments by increasing the number of research studies underway in any particular healthcare field,鈥� says Ghaffaripour.

Going forward, Ghaffaripour and Miri will explore further applications of this research to verifiable machine-learning models that offer medical diagnostic services designed around mutual privacy protection mechanisms.