Abstract
Recent proposals in data anonymization have mostly been focused around MapReduce, though the advantages of Spark have been well documented. To address this concern, we propose a new novel data anonymization technique for Apache Spark. SparkDA, our proposal, takes the full advantages of innovative Spark features, such as better partition control, in-memory process, and cache management for iterative operations, while providing high data utility with privacy. These are achieved by proposing data anonymization algorithms through Spark’s Resilient Distributed Dataset (RDD). Our data anonymization algorithms are implemented at two main data processing RDD transformations, FlatMapRDD and ReduceByKeyRDD, respectively. Our experimental results show that our proposed approach provides required data privacy and utility levels while providing scalability with high-performance that are essential to many large datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Central Statistics Office (Internet) (2011). http://www.cso.ie/en/databases/. Accessed 16 Aug 2019
Al-Zobbi, M., Shahrestani, S., Ruan, C.: Sensitivity-based anonymization of big data. In: IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), pp. 58–64. IEEE (2016)
Antonatos, S., Braghin, S., Holohan, N., Gkoufas, Y., Mac Aonghusa, P.: Prima: an end-to-end framework for privacy at scale. In: IEEE 34th International Conference on Data Engineering (ICDE), pp. 1531–1542. IEEE (2018)
Asuncion, A., Newman, D.: UCI machine learning repository (2007). http://archive.ics.uci.edu/ml. Accessed 16 July 2019
Ayala-Rivera, V., McDonagh, P., Cerqueus, T., Murphy, L.: Synthetic data generation using benerator tool. arXiv preprint arXiv:1311.3312 (2013)
Ayala-Rivera, V., McDonagh, P., Cerqueus, T., Murphy, L., et al.: A systematic comparison and evaluation of k-anonymization algorithms for practitioners. Trans. Data Priv. 7(3), 337–370 (2014)
Bazai, S.U., Jang-Jaccard, J., Wang, R.: Anonymizing k-NN classification on MapReduce. In: Hu, J., Khalil, I., Tari, Z., Wen, S. (eds.) MONAMI 2017. LNICST, vol. 235, pp. 364–377. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90775-8_29
Bazai, S.U., Jang-Jaccard, J., Zhang, X.: A privacy preserving platform for MapReduce. In: Batten, L., Kim, D.S., Zhang, X., Li, G. (eds.) ATIS 2017. CCIS, vol. 719, pp. 88–99. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5421-1_8
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Grolinger, K., Hayes, M., Higashino, W.A., L’Heureux, A., Allison, D.S., Capretz, M.A.: Challenges for MapReduce in big data. In: IEEE World Congress on Services, pp. 182–189. IEEE (2014)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R., et al.: Mondrian multidimensional k-anonymity. In: ICDE, vol. 6, p. 25 (2006)
Shi, J., et al.: Clash of the titans: MapReduce vs. spark for large scale data analytics. Proc. VLDB Endow. 8(13), 2110–2121 (2015)
Sopaoglu, U., Abul, O.: A top-down k-anonymization implementation for apache Spark. In: IEEE International Conference on Big Data (Big Data), pp. 4513–4521. IEEE (2017)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(05), 571–588 (2002)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10–10), 95 (2010)
Zhang, X., Liu, C., Nepal, S., Yang, C., Dou, W., Chen, J.: Combining top-down and bottom-up: scalable sub-tree anonymization over big data using MapReduce on cloud. In: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 501–508. IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bazai, S.U., Jang-Jaccard, J. (2019). SparkDA: RDD-Based High-Performance Data Anonymization Technique for Spark Platform. In: Liu, J., Huang, X. (eds) Network and System Security. NSS 2019. Lecture Notes in Computer Science(), vol 11928. Springer, Cham. https://doi.org/10.1007/978-3-030-36938-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-36938-5_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36937-8
Online ISBN: 978-3-030-36938-5
eBook Packages: Computer ScienceComputer Science (R0)