Secure and Privacy-Preserving Edge Computing Framework for Building Energy Analytics
Abstract
Abstract: Building energy analytics involve the processing of sensitive data, making security and privacy crucial considerations. This paper presents a secure and privacy-preserving edge computing framework for building energy analytics. We propose an architecture that integrates cryptographic techniques, secure data aggregation, and privacy-preserving algorithms into edge computing systems. Our framework ensures the confidentiality, integrity, and privacy of energy-related data while enabling real-time analytics and decision-making. Through performance evaluations and security analysis, we demonstrate the effectiveness and scalability of our approach. We also discuss the potential trade-offs between security, privacy, and computational overhead. Our research provides valuable insights for system designers, policymakers, and stakeholders interested in deploying secure and privacy-preserving energy analytics solutions in building environments.
References
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