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Smart-meter data often contain missing intervals that hamper analytics, billing, and demand response verification. Privacy regulations and consumer confidentiality expectations further restrict access to raw grid-edge data, even though accurate reconstruction remains essential for operational decision-making. Existing imputation methods may restore missing values but often ignore the physical constraints of electricity demand. At the same time, methods that rely on real customer records can create privacy risks when individual consumption patterns are exposed or reconstructed too precisely. This article proposes a conceptual physics-guided generative framework for reconstructing missing smart-meter data using synthetic load profiles. The framework is designed to avoid dependence on real consumer records while supporting demand response verification. The proposed framework could combine generative modelling with physical and operational constraints such as non-negative demand, ramp-rate limits, and daily periodic load patterns. It would use synthetic load profiles to represent realistic demand behaviour while preserving privacy. Benchmark approaches are discussed qualitatively rather than experimentally. The expected behaviour of the framework is described in terms of reconstruction fidelity, privacy preservation, physical consistency, and demand response verification usefulness. Four original tables summarise missing-data impacts, privacy risks, qualitative benchmark comparisons, and proposed evaluation metrics. The proposed framework offers a privacy-preserving and physics-aware pathway for smart-meter data reconstruction. It is intended as a conceptual foundation for future validation using synthetic, public, or utility-approved datasets.