00000cam 2200000zi 4500 9.966537 CaOODSP 20261006104434 m o d f cr cn||||||||| 261006e20250908oncad ob f000 0 eng d CaOODSP eng rda CaOODSP n-cn--- CS11-522/2024-1-17E-PDF Santos, Benjamin, author. Exploring of deep learning synthetic data generation for sensitive utility data sharing / by Benjamin Santos, Rafik Chemli, and Julian Templeton. At head of title: Proceedings of Statistics Canada Symposium 2024 : the future of official statistics [Ottawa] : Statistics Canada = Statistique Canada, September 8, 2025. 1 online resource (8 unnumbered pages) : illustrations, graphs. text txt rdacontent computer c rdamedia online resource cr rdacarrier [Statistics Canada international symposium series : proceedings], 1709-8211 Title from cover. Issued 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. Includes bibliographical references. "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]. Confidential communications Statistical services Canada. Deep learning (Machine learning) Information storage and retrieval systems Public utilities. Statistics Canada, issuing body. Exploration de la génération de données synthétiques d'apprentissage profond pour partager des données sensibles liées à des services publics / (CaOODSP)9.966540 Statistics Canada international symposium series - proceedings, 1709-8211 (CaOODSP)9.505681 PDF 580 KB https://publications.gc.ca/collections/collection_2026/statcan/11-522-x/CS11-522-2024-1-17-eng.pdf 11-522-X