paper + CDRs-based dataset (Aggregated mobility)

[hharcolezi]

• The BibTex of the paper: @article{Arcolezi2022, doi = {10.1007/s00521-022-07393-0}, url = {https://doi.org/10.1007/s00521-022-07393-0}, year = {2022}, month = jun, publisher = {Springer Science and Business Media {LLC}}, author = {H{'{e}}ber Hwang Arcolezi and Jean-Fran{\c{c}}ois Couchot and Denis Renaud and Bechara Al Bouna and Xiaokui Xiao}, title = {Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?}, journal = {Neural Computing and Applications} }
• The name of the model proposed: No new model (evaluation of state-of-the-art shallow RNNs with differential privacy guarantees)
• The DL components adopted in the model: LSTM, GRU, BiLSTM, BiGRU
• The evaluation metrics adopted: RMSE and privacy-utility trade-off
• A link to an open datasets - https://github.com/hharcolezi/ldp-protocols-mobility-cdrs/blob/main/papers/%5B3%5D/ML_final_df_real.csv
• A link to a code repository - https://github.com/hharcolezi/ldp-protocols-mobility-cdrs/tree/main/papers/%5B3%5D

For a dataset

• The BibTex of the publication or the link related to the datasets: Same as before
• Dataset type: multivariate time series (6 coarse regions by 30-min time interval)
• The spatial and temporal coverage: Paris, France from Aug. 2020 to Nov. 2020
• The number of subjects covered: On average, 100k unique users per time interval
• The accessibility conditions: Fully open, with proper reference.
• A link to the dataset: https://github.com/hharcolezi/ldp-protocols-mobility-cdrs/blob/main/papers/%5B3%5D/ML_final_df_real.csv