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Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?
This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training dee...
Autores principales: | Arcolezi, Héber Hwang, Couchot, Jean-François, Renaud, Denis, Al Bouna, Bechara, Xiao, Xiaokui |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162903/ https://www.ncbi.nlm.nih.gov/pubmed/35677085 http://dx.doi.org/10.1007/s00521-022-07393-0 |
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