Location-Aware and Privacy-Preserving Data Cleaning for Intelligent Transportation

The widespread use of machine learning in location-related scenarios is propelling the rapid development of intelligent transportation. To assist users in making more informed travel plans, the demand for improving prediction accuracy is growing. Prior to model training, data cleaning is a common method used to eliminate redundant, erroneous and outlier samples. However, in intelligent transportation, there are serious issues with location awareness and privacy protection of existing data cleaning schemes. Therefore, we propose a location-aware and privacy-preserving data cleaning framework (PriSPA) which provides a cleaned dataset consisting of the samples from adopted data suppliers at qualified locations while ensuring the privacy of locations, spatial constraints and sensitive samples. We combine boolean secret sharing with XOR operations to make sure that it is possible to figure out whether a location complies with spatial constraints without leakage. More specifically, we ensure privacy using key agreement, secret sharing, authenticated encryption and random permutation. We seriously analyze the security of PriSPA and conduct comprehensive experiments to prove its security, effectiveness and efficiency. Based on the comparisons with the raw traffic forecasting framework, we observe that PriSPA improves the precision of the model with 17.6% - 32.7% error reduction.