Abstract
Cloud computing provides promising scalable IT infrastructure to support various processing of a variety of big data applications in sectors such as healthcare and business. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared to third-parties in cloud. A practical and widely-adopted technique for data privacy preservation is to anonymize data via generalization to satisfy a given privacy model. However, most existing privacy preserving approaches tailored to small-scale data sets often fall short when encountering big data, due to their insufficiency or poor scalability. In this paper, we investigate the local-recoding problem for big data anonymization against proximity privacy breaches and attempt to identify a scalable solution to this problem. Specifically, we present a proximity privacy model with allowing semantic proximity of sensitive values and multiple sensitive attributes, and model the problem of local recoding as a proximity-aware clustering problem. A scalable two-phase clustering approach consisting of a t-ancestors clustering (similar to k-means) algorithm and a proximity-aware agglomerative clustering algorithm is proposed to address the above problem. We design the algorithms with MapReduce to gain high scalability by performing data-parallel computation in cloud. Extensive experiments on real-life data sets demonstrate that our approach significantly improves the capability of defending the proximity privacy breaches, the scalability and the time-efficiency of local-recoding anonymization over existing approaches.
Original language | English |
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Pages (from-to) | 2293-2307 |
Number of pages | 15 |
Journal | IEEE Transactions on Computers |
Volume | 64 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2015 |
Externally published | Yes |
Keywords
- Big data
- cloud computing
- mapreduce
- data anonymization
- proximity privacy
- Cloud Computing
- Proximity Privacy
- Big Data
- Data Anonymization
- MapReduce