Abstract
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 (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.
Highlights
Intelligent energy systems and so-called smart grids, change the way electricity is generated, distributed, and used
A wavelet transform starts with the original load curve m = (m1, m2, . . . , mT ), which denotes a series of values
By contrast, still works with the aggregate, but requires a much higher temporal resolution. Both homomorphic encryption and masking can be used for aggregating over a number of smart meters, e.g., from households connected to the same substation or participants belonging to the same consumption group
Summary
Intelligent energy systems and so-called smart grids, change the way electricity is generated, distributed, and used. 1.3 Contribution In this paper, a set of three privacy-preserving smart metering data aggregation methods that combine the two types of approaches, namely, multi-resolution representation and (i) homomorphic encryption; (ii) masking; and (iii) differential privacy, is proposed. This improves the capabilities for managing privacy requirements, as the combination of “traditional” privacy-enhancing methods with multi-resolution representation significantly increases the choices available for both system operator and end-user. In section Background, background is presented on wavelets for the multi-resolution representation of load curves as well as on the three privacy-enhancing technologies (PETs) homomorphic encryption, masking, and differential privacy. We further assume all devices to be tamper-proof, i.e., the meter value itself cannot be manipulated
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