A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud

•We investigate the scalability problem of sub-tree anonymization of big data in cloud.•A hybrid approach containing top–down specialization and bottom–up generalization.•Innovative MapReduce jobs are designed for computation in bottom–up generalization.•Multiple generalizations are performed simultaneously in each iteration for scalability.•Data skewness is integrated when estimating the workload balancing point.

In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches.