Privacy-preserving outsourced collaborative frequent itemset mining in the cloud

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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
EditorsZoran Obradovic, Ricardo Baeza-Yates, Jeremy Kepner, Raghunath Nambiar, Chonggang Wang, Masashi Toyoda, Toyotaro Suzumura, Xiaohua Hu, Alfredo Cuzzocrea, Ricardo Baeza-Yates, Jian Tang, Hui Zang, Jian-Yun Nie, Rumi Ghosh
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4827-4829
Number of pages3
Volume2018-January
ISBN (Electronic)9781538627143
DOIs
StatePublished - 12 Jan 2018
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: 11 Dec 201714 Dec 2017

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. (2018). Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. In Z. Obradovic, R. Baeza-Yates, J. Kepner, R. Nambiar, C. Wang, M. Toyoda, T. Suzumura, X. Hu, A. Cuzzocrea, R. Baeza-Yates, J. Tang, H. Zang, J-Y. Nie, ... R. Ghosh (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (Vol. 2018-January, pp. 4827-4829). 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 / Zoran Obradovic ; Ricardo Baeza-Yates ; Jeremy Kepner ; Raghunath Nambiar ; Chonggang Wang ; Masashi Toyoda ; Toyotaro Suzumura ; Xiaohua Hu ; Alfredo Cuzzocrea ; Ricardo Baeza-Yates ; Jian Tang ; Hui Zang ; Jian-Yun Nie ; Rumi Ghosh. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4827-4829
@inproceedings{9aa0769ffd2b4128aa0c8a04375d5d6d,
title = "Privacy-preserving outsourced collaborative frequent itemset mining in the cloud",
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.",
keywords = "cloud, encryption, frequent itemsets, privacy",
author = "Samanthula, {Bharath Kumar}",
year = "2018",
month = "1",
day = "12",
doi = "10.1109/BigData.2017.8258556",
language = "English",
volume = "2018-January",
pages = "4827--4829",
editor = "Zoran Obradovic and Ricardo Baeza-Yates and Jeremy Kepner and Raghunath Nambiar and Chonggang Wang and Masashi Toyoda and Toyotaro Suzumura and Xiaohua Hu and Alfredo Cuzzocrea and Ricardo Baeza-Yates and Jian Tang and Hui Zang and Jian-Yun Nie and Rumi Ghosh",
booktitle = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Samanthula, BK 2018, Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. in Z Obradovic, R Baeza-Yates, J Kepner, R Nambiar, C Wang, M Toyoda, T Suzumura, X Hu, A Cuzzocrea, R Baeza-Yates, J Tang, H Zang, J-Y Nie & R Ghosh (eds), 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. / Zoran Obradovic; Ricardo Baeza-Yates; Jeremy Kepner; Raghunath Nambiar; Chonggang Wang; Masashi Toyoda; Toyotaro Suzumura; Xiaohua Hu; Alfredo Cuzzocrea; Ricardo Baeza-Yates; Jian Tang; Hui Zang; Jian-Yun Nie; Rumi Ghosh. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 4827-4829.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Privacy-preserving outsourced collaborative frequent itemset mining in the cloud

AU - Samanthula, Bharath Kumar

PY - 2018/1/12

Y1 - 2018/1/12

N2 - 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.

AB - 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.

KW - cloud

KW - encryption

KW - frequent itemsets

KW - privacy

UR - http://www.scopus.com/inward/record.url?scp=85047780546&partnerID=8YFLogxK

U2 - 10.1109/BigData.2017.8258556

DO - 10.1109/BigData.2017.8258556

M3 - Conference contribution

VL - 2018-January

SP - 4827

EP - 4829

BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Obradovic, Zoran

A2 - Baeza-Yates, Ricardo

A2 - Kepner, Jeremy

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Toyoda, Masashi

A2 - Suzumura, Toyotaro

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Baeza-Yates, Ricardo

A2 - Tang, Jian

A2 - Zang, Hui

A2 - Nie, Jian-Yun

A2 - Ghosh, Rumi

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Samanthula BK. Privacy-preserving outsourced collaborative frequent itemset mining in the cloud. In Obradovic Z, Baeza-Yates R, Kepner J, Nambiar R, Wang C, Toyoda M, Suzumura T, Hu X, Cuzzocrea A, Baeza-Yates R, Tang J, Zang H, Nie J-Y, Ghosh R, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4827-4829 https://doi.org/10.1109/BigData.2017.8258556