| Title | A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ghaffaripour, S., Miri, A. |
| Conference Name | 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-8526-2 |
| Keywords | blockchain, blockchains, commit-and-prove, commitment schemes, crowdsourcing, crowdsourcing platform, cryptography, cybernetics, data privacy, decentralized platform, Distributed databases, distributed processing, faces, health data privacy, human factors, machine learning, medical computing, medical research, mutual trust, policy-based governance, privacy, pubcrawl, resilience, Resiliency, Scalability, smart contracts, Trusted Computing, verifiable computation, zero knowledge argument of knowledge, zero trust, zero-knowledge proof, ZK-SNARK |
| Abstract | Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients' data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers. |
| URL | https://ieeexplore.ieee.org/document/9283027 |
| DOI | 10.1109/SMC42975.2020.9283027 |
| Citation Key | ghaffaripour_decentralized_2020 |
A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research