Supporting Privacy-Preserving Big Data Analytics on Temporal Open Big Data

Abstract

Nowadays, valuable big data are generated and collected rapidly from numerous rich data sources. Following the initiatives of open data, many organizations including municipal governments are willing to share their data such as open big data regarding parking violations. While there have been models to preserve privacy of sensitive personal data like patient data for health informatics, privacy of individuals who violated parking regulations should also be protected. Hence, in this article, we present a model for supporting privacy-preserving big data analytics on temporal open big data. This temporally hierarchical privacy-preserving model (THPPM) adapts and extends the traditional temporal hierarchy to generalize spatial data generated within a time interval with an aim to preserve privacy of individuals who violated parking regulations during some time intervals at certain geographic locations. Evaluation on open big data from two North American cities demonstrates the usefulness of our model in supporting privacy-preserving big data analytics on temporal open big data.