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Efficient Privacy-Preserving Framework for Federated and Blockchain Networks
  • Peyman Khordadpour
Peyman Khordadpour
Independent Researcher

Corresponding Author:[email protected]

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Abstract

Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.