Multi-level privacy preserving data publishing. (2018) - Gao, Zhiqiang; Wang, Yutao; Duan, Yanyu; Wang, Yun; Peng, Zhensheng
Policedata is an important source of social media data and can be regarded as a technical assistance to increase government accountability and transparency. Notably, it contains large amounts of personal private information that should be preserved deliberately. However, sharing and publishing policedata through private or public cloud infrastructure are still faced with tremendously potential threats and challenges recently. Unfortunately, existing researches regarding privacy preserving data publishing (PPDP) fail to cope with the aforementioned problems. Our work aims to propose a systematic multi-level privacy preserving data publishing ( ML -PPDP) architecture. Moreover, a personalised multi-level privacy preserving ( pML -PPDP) mechanism that developed from the combination of k -anonymity, l-diversity, t-closeness and differential privacy is designed for policedata publishing. Our solution authorised users with different privileges to different privacy-preserving levels. Experimental results of pML -PPDP mechanism on datasets collected from policedata website are implemented under our proposed ML -PPDP architecture with satisfactory trade-off between privacy and utility.