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Corresponding Author

Amr Tarek Elsayed

Document Type

Review Article

Subject Areas

Mathematics and Statistics

Keywords

Smart homes, Local differential privacy, Privacy-preserving, Gaussian noise, SOMTe, Security

Abstract

Smart homes represent intelligent environments where interconnected devices gather information, enhancing users’ living experiences by ensuring comfort, safety, and efficient energy management. To enhance the quality of life, companies in the smart device industry collect user data, including activities, preferences, and power consumption. However, sharing such data necessitates privacy-preserving practices. This paper introduces a robust method for secure sharing of data to service providers, grounded in differential privacy (DP). This empowers smart home residents to contribute usage statistics while safeguarding their privacy. The approach incorporates the Synthetic Minority Oversampling technique (SMOTe) and seamlessly integrates Gaussian noise to generate synthetic data, enabling data and statistics sharing while preserving individual privacy. The proposed method employs the SMOTe algorithm and applies Gaussian noise to generate data. Subsequently, it employs a k-anonymity function to assess re-identification risk before sharing the data. The simulation outcomes demonstrate that our method delivers strong performance in safeguarding privacy and in accuracy, recall, and f-measure metrics. This approach is particularly effective in smart homes, offering substantial utility in privacy at a re-identification risk of 30%, with Gaussian noise set to 0.3, SMOTe at 500%, and the application of a k-anonymity function with k ¼ 2. Additionally, it shows a high classification accuracy, ranging from 90% to 98%, across various classification techniques.

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