Blockchain is a decentralized digital ledger consisting of blocks that are chained together via cryptographic hash functions. A new study by TSYS School professor Yesem Kurt Peker and colleagues from TOBB University in Turkey proposes a blockchain-based architecture that uses smart contracts and homomorphic encryption to allow statistical computations on confidential data by third parties. As they explain in the study, which appears in the Journal of Millimeterwave Communication, Optimization and Modelling, the use of blockchain provides the much-desired security properties of integrity and fault tolerance while homomorphic encryption preserves the privacy of the data. Results discussed by the researchers show that a blockchain-based data sharing mechanism with homomorphic calculations via a smart contract is feasible and provides improvements in protecting the data from unauthorized users. "Even though our work focused on linear regression, the architecture can be used for other statistical analysis and machine learning algorithms," Peker explained.
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