00000000cam 2200000zi 4500
0019.966537
003CaOODSP
00520261006104434
006m     o  d f      
007cr cn|||||||||
008261006e20250908oncad   ob   f000 0 eng d
040 |aCaOODSP|beng|erda|cCaOODSP
043 |an-cn---
0861 |aCS11-522/2024-1-17E-PDF
1001 |aSantos, Benjamin, |eauthor.
24510|aExploring of deep learning synthetic data generation for sensitive utility data sharing / |cby Benjamin Santos, Rafik Chemli, and Julian Templeton.
2461 |iAt head of title: |aProceedings of Statistics Canada Symposium 2024 : |bthe future of official statistics
264 1|a[Ottawa] : |bStatistics Canada = Statistique Canada, |cSeptember 8, 2025.
300 |a1 online resource (8 unnumbered pages) : |billustrations, graphs.
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |a[Statistics Canada international symposium series : proceedings], |x1709-8211
500 |aTitle from cover.
500 |aIssued also in French under title: Exploration de la génération de données synthétiques d'apprentissage profond pour partager des données sensibles liées à des services publics.
504 |aIncludes bibliographical references.
5203 |a"Utilities hold crucial information about energy usage and building characteristics which can be utilized by government agencies to improve their corresponding analytics. However, this data is associated with private customer records and thus the building data and energy usage may be too sensitive to share. Often, high-level aggregated versions of this data are shared through robust contracts, limiting the statistics that can be derived. With the advancement of generative machine learning techniques, Statistics Canada and Natural Resources Canada have explored the feasibility of using these models to produce synthetic versions of utility data which may be shared in full to requesting organizations"--Abstract, page [2].
650 0|aConfidential communications|xStatistical services|zCanada.
650 0|aDeep learning (Machine learning)
650 0|aInformation storage and retrieval systems|xPublic utilities.
7102 |aStatistics Canada, |eissuing body.
77508|tExploration de la génération de données synthétiques d'apprentissage profond pour partager des données sensibles liées à des services publics / |w(CaOODSP)9.966540
830#0|aStatistics Canada international symposium series - proceedings,|x1709-8211|w(CaOODSP)9.505681
85640|qPDF|s580 KB|uhttps://publications.gc.ca/collections/collection_2026/statcan/11-522-x/CS11-522-2024-1-17-eng.pdf
986 |a11-522-X