SMASK: Parallel Probabilistic Privacy-Preserving Frequent Pattern Mining Technique for Big Data

This paper introduces a spark-based fast solution for privacy-preserving frequent pattern mining problems for big data. Spark Resilient Distributed Dataset (RDD) framework has been used to implement the Mask approach, which uses the probabilistic distortion method for maintaining data privacy while mining frequent patterns. The masking technique shows very promising results in terms of privacy and utility both. However, due to sequential nature limits the application to small or medium size data. The spark-based proposed technique introduces two-level parallelization i.e. data and algorithmic level which in turn paves a way to gain faster analytical results in a bounded amount of time while dealing with a large volume of datasets. This makes the application feasible for the current growth of data size. A number of experiments have been conducted to compare the performance of the proposed scheme with benchmark parallel approaches in terms of privacy, utility, and time complexity over real and simulated data sets. It has been observed that the proposed scheme preserves the privacy of sensitive data while maintaining utility within a real bound of time. Experiments show that the proposed Spark-based scheme i.e. S-Mask gains 16 times speedup on average over different benchmark data sets and maintains a desired ratio between privacy and utility of the data.