Privacy-preserving outsourced collaborative frequent itemset mining in the cloud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Big Data management and analytics has revolutionized the way how organizations collect, store, process and retrieve, huge volumes of data. In order to fully leverage the potential of big data, it is often that organizations need to collaborate and analyze their combined data, and thus, improving the accuracy of results. However, due to government regulations and internal privacy policies, organizations cannot freely share their data with one another. Existing secure multiparty computation techniques along this direction are very expensive. In this paper, we develop a protocol that facilitates multiple users to outsource their encrypted databases as well as the frequent itemset mining task to a cloud environment in a collaborative and privacy-preserving manner. Our solution is built using the well-known apriori algorithm in order to boost the performance of frequent itemset mining in the cloud. Our comprehensive analysis has demonstrated that the proposed solution preserves the confidentiality of participating users. Additionally, our solution ensures that the entire frequent itemset mining task is performed on the cloud-side, thereby fully utilizing the cloud computing services to handle big data needs and incurring negligible cost on the end-users.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4827-4829
Number of pages3
ISBN (Electronic)9781538627143
DOIs
StatePublished - 1 Jul 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: 11 Dec 201714 Dec 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period11/12/1714/12/17

Fingerprint

Frequent Itemsets
Privacy Preserving
Mining
Cloud computing
Information management
Apriori Algorithm
Secure multi-party Computation
Confidentiality
Data Management
Cloud Computing
Leverage
Privacy
Big data
Privacy preserving
Costs
Entire
Internal

Keywords

  • cloud
  • encryption
  • frequent itemsets
  • privacy

Cite this

Samanthula, B. K. (2017). Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, ... M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 4827-4829). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258556
Samanthula, Bharath Kumar. / Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4827-4829 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).
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Samanthula, BK 2017, Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 4827-4829, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 11/12/17. https://doi.org/10.1109/BigData.2017.8258556

Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. / Samanthula, Bharath Kumar.

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4827-4829 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Samanthula BK. Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. In Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4827-4829. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). https://doi.org/10.1109/BigData.2017.8258556