RCDP: A Privacy-Preserving Approach for Synthesizing Realistic Commuting Data

Xiaoying Liu, Juanjuan Zhao, Xitong Gao, Kejiang Ye

Abstract: Publishing commuting trajectory data, including information of home and workplace locations, commuting distances and working hours, provides valuable insights for urban transportation planning. However, the data also contains sensitive personal information, raising privacy concerns even after the removal of unique identifiers. While traditional privacy-preserving methods, such as k-anonymity and differential privacy, have been widely applied, they mainly focus on single-trip travel patterns (e.g., point sequences or paths) and fail to capture the unique characteristics of commuting behavior. In this paper, we propose RCDP, a novel differential privacy-based model for synthesizing realistic commuting data using a prefix tree structure. RCDP introduces two key innovations: (1) it models round-trip commuting patterns through adaptive spatio-temporal generalization and a prefix tree, ensuring that the synthesized data retains the key commuting characteristics; (2) it employs a hierarchical privacy budget allocation mechanism that dynamically adjusts the budget across tree levels, along with a distribution-based node insertion method to maintain tree consistency, effectively balancing privacy and utility. Validation using public transport smart card data in Shenzhen, China demonstrates that RCDP outperforms existing k-anonymity and differential privacy approaches in preserving essential commuting features while ensuring strong privacy protection.

External IDs:dblp:conf/bigdataconf/LiuZGY24