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Privacy, Informed Consent and the Demand for Anonymisation of Smart Meter Data
Authors:
Saurab Chhachhi,
Fei Teng
Abstract:
Access to smart meter data offers system-wide benefits but raises significant privacy concerns due to the personal information it contains. Privacy-preserving techniques could facilitate wider access, though they introduce privacy-utility trade-offs. Understanding consumer valuations for anonymisation can help identify appropriate trade-offs. However, existing studies do not focus on anonymisation…
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Access to smart meter data offers system-wide benefits but raises significant privacy concerns due to the personal information it contains. Privacy-preserving techniques could facilitate wider access, though they introduce privacy-utility trade-offs. Understanding consumer valuations for anonymisation can help identify appropriate trade-offs. However, existing studies do not focus on anonymisation specifically or account for information asymmetries regarding privacy risks, raising questions about the validity of informed consent under current regulations.
We use a mixed-methods approach to estimate non-monetary (willingness-to-share and smart metering demand) and monetary (willingness-to-pay/accept) preferences for anonymisation, based on a representative sample of 965 GB bill payers. An embedded randomised control trial examines the effect of providing information about privacy implications.
On average, consumers are willing to pay for anonymisation, are more willing to share data when anonymised and less willing to share non-anonymised data once anonymisation is presented as an option. However, a significant minority remains unwilling to adopt smart meters, despite anonymisation. We find strong evidence of information asymmetries that suppress demand for anonymisation and identify substantial variation across demographic and electricity supply characteristics. Qualitative responses corroborate the quantitative findings, underscoring the need for stronger privacy defaults, user-centric design, and consent mechanisms that enable truly informed decisions.
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Submitted 27 August, 2025;
originally announced September 2025.
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A Joint Energy and Differentially-Private Smart Meter Data Market
Authors:
Saurab Chhachhi,
Fei Teng
Abstract:
Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. Th…
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Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. This paper proposes a joint energy and data market focusing on the day-ahead retailer energy procurement problem with uncertain demand. The retailer can purchase differentially-private smart meter data from consumers to reduce uncertainty. The problem is modelled as an integrated forecasting and optimisation problem providing a means of valuing data directly rather than valuing forecasts or forecast accuracy. Value is determined by the Wasserstein distance, enabling privacy to be preserved during the valuation and procurement process. The value of joint energy and data clearing is highlighted through numerical case studies using both synthetic and real smart meter data.
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Submitted 10 December, 2024;
originally announced December 2024.
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Wasserstein Markets for Differentially-Private Data
Authors:
Saurab Chhachhi,
Fei Teng
Abstract:
Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted third part…
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Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted third party to perform computationally expensive valuations or are unable to capture the combinatorial nature of data value and do not endogenously model the effect of differential privacy. This paper addresses these shortcomings by proposing a valuation mechanism based on the Wasserstein distance for differentially-private data, and corresponding procurement mechanisms by leveraging incentive mechanism design theory, for task-agnostic data procurement, and task-specific procurement co-optimisation. The mechanisms are reformulated into tractable mixed-integer second-order cone programs, which are validated with numerical studies.
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Submitted 3 December, 2024;
originally announced December 2024.
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On the 1-Wasserstein Distance between Location-Scale Distributions and the Effect of Differential Privacy
Authors:
Saurab Chhachhi,
Fei Teng
Abstract:
We provide an exact expressions for the 1-Wasserstein distance between independent location-scale distributions. The expressions are represented using location and scale parameters and special functions such as the standard Gaussian CDF or the Gamma function. Specifically, we find that the 1-Wasserstein distance between independent univariate location-scale distributions is equivalent to the mean…
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We provide an exact expressions for the 1-Wasserstein distance between independent location-scale distributions. The expressions are represented using location and scale parameters and special functions such as the standard Gaussian CDF or the Gamma function. Specifically, we find that the 1-Wasserstein distance between independent univariate location-scale distributions is equivalent to the mean of a folded distribution within the same family whose underlying location and scale are equal to the difference of the locations and scales of the original distributions. A new linear upper bound on the 1-Wasserstein distance is presented and the asymptotic bounds of the 1-Wasserstein distance are detailed in the Gaussian case. The effect of differential privacy using the Laplace and Gaussian mechanisms on the 1-Wasserstein distance is studied using the closed-form expressions and bounds.
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Submitted 28 April, 2023;
originally announced April 2023.
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Market Value of Differentially-Private Smart Meter Data
Authors:
Saurab Chhachhi,
Fei Teng
Abstract:
This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on th…
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This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy.
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Submitted 20 April, 2021;
originally announced April 2021.