Differentially Private Publication of Electricity Time Series Data in Smart Grids
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929478208323584 |
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| author | Shaham, Sina Ghinita, Gabriel Krishnamachari, Bhaskar Shahabi, Cyrus |
| author_facet | Shaham, Sina Ghinita, Gabriel Krishnamachari, Bhaskar Shahabi, Cyrus |
| contents | Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g., transformers, storage elements) and their activation schedules. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. Differential privacy (DP) is well-suited for sanitization of individual data, but current DP techniques for time series lead to significant loss in utility, due to the existence of temporal correlation between data readings. We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs. Additionally, it employs a partitioning method for releasing electricity consumption time series based on identified patterns. We demonstrate through extensive experiments, on both real-world and synthetic datasets, that STPT significantly outperforms existing benchmarks, providing a well-balanced trade-off between data utility and user privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentially Private Publication of Electricity Time Series Data in Smart Grids Shaham, Sina Ghinita, Gabriel Krishnamachari, Bhaskar Shahabi, Cyrus Cryptography and Security Artificial Intelligence Machine Learning Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g., transformers, storage elements) and their activation schedules. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. Differential privacy (DP) is well-suited for sanitization of individual data, but current DP techniques for time series lead to significant loss in utility, due to the existence of temporal correlation between data readings. We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs. Additionally, it employs a partitioning method for releasing electricity consumption time series based on identified patterns. We demonstrate through extensive experiments, on both real-world and synthetic datasets, that STPT significantly outperforms existing benchmarks, providing a well-balanced trade-off between data utility and user privacy. |
| title | Differentially Private Publication of Electricity Time Series Data in Smart Grids |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.16017 |