Sep 2026· Computing, Communication, and Sustainable Energy Technologies· pp. 103-106
Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security
Abstract
As AI adoption grows in personal data management and healthcare, privacy risks from data breaches escalate. Traditional models rely on sensitive data for training, exposing users to vulnerabilities. Blockchain offers a decentralized, immutable solution with encryption and no central authority to mitigate these issues. This work develops blockchain-driven AI models for privacy-preserving data exchange. A smart contract framework automates access control, verifying computations on encrypted data. Federated learning and homomorphic encryption enable distributed training without exposing raw data. Evaluated in regulated healthcare systems, the approach ensures scalability, security, and compliance. Results demonstrate enhanced trust in AI, balancing innovation with privacy regulations for responsible data-driven solutions.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
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It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
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Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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