A multi-objective optimization method generates privacy-preserving synthetic daily mobility trajectories from aggregated OD data, dwell-travel quantiles, and the universal visit-frequency law that match real distributions in Tokyo and Fukuoka.
On the difficulty of achieving differential privacy in practice: user-level guarantees in aggregate location data
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Privacy-Preserving Synthetic Dataset of Individual Daily Trajectories for City-Scale Mobility Analytics
A multi-objective optimization method generates privacy-preserving synthetic daily mobility trajectories from aggregated OD data, dwell-travel quantiles, and the universal visit-frequency law that match real distributions in Tokyo and Fukuoka.