Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept.

ABSTRACT:

Background

The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party.

Objective

This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques.

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SUBMITTER: Jones M 

PROVIDER: S-EPMC6712958 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature