Demonstrates effective data protection in smart meters, suggesting a new privacy framework.
To address the privacy risks of high-frequency and fine-grained elec-tricity data collected by smart meters, which may expose user be-havior patterns and lifestyle habits, this study proposes a smart me-ter data protection model that integrates K-modes clustering–basedshuffled differential privacy with a time auto-wave neural network(TANN) for TOP-K queries.
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Chen et al. (2026) studied this question.
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