The availability of individual load profiles per household in the smart grid end-user domain combined with non-intrusive load monitoring to infer personal data from these load curves has led to privacy concerns. Privacy-enhancing technologies have been proposed to address these concerns. In this paper, the extension of privacy-enhancing technologies by wavelet-based multi-resolution analysis (MRA) is proposed to enhance the options available on the user side. For three types of privacy methods

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... secure aggregation, masking and differential privacy), we show that MRA not only enhances privacy, but also adds additional flexibility and control for the end-user. The combination of MRA and PETs is evaluated in terms of privacy, computational demands, and real-world feasibility for each of the three method types.