The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles
arXiv:2606.03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns. While this risk has been demonstrated at fixed granularities, the role of temporal resolution in shaping inference performance remains insufficiently explored. This paper addresses this gap by analyzing how load profiles with granularities from 15 minutes to 7 days affect the predictability of eight socio-demographic attributes in a dataset of 1,589 households over one year. We introduce an evaluation framework where classifiers are tr
The proliferation of smart meters and the increasing sophistication of AI for data analysis make socio-demographic inference a pressing concern for privacy and security. This research directly addresses the nuanced role of data granularity in that inference.
This research provides critical insights for policymakers, smart grid operators, and privacy advocates on the specific risks associated with smart meter data at various temporal resolutions. It informs the development of privacy-preserving technologies and regulations.
The understanding of how temporal resolution impacts the predictability of sensitive socio-demographic data from household load profiles is clarified, allowing for more targeted privacy interventions. It suggests that even aggregated data can pose risks.
- · Privacy advocates
- · Cybersecurity firms
- · Regulatory bodies
- · Consumers seeking data protection
- · Data brokers
- · Companies with lax data anonymization practices
- · Marketing firms relying on inferred household data
Increased pressure on utilities and smart device manufacturers to implement more robust data anonymization and privacy-by-design standards.
Development of new privacy-enhancing technologies specifically designed to obscure socio-demographic inferences from energy consumption patterns.
Potential for new legal frameworks or class-action lawsuits based on privacy violations stemming from smart meter data exploitation, influencing national data sovereignty discussions.
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Read at arXiv cs.LG