research-article

A secure multiparty computation privacy preserving OLAP framework over distributed XML data

Authors:

Alfredo Cuzzocrea,

Elisa BertinoAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 1666 - 1673

https://doi.org/10.1145/1774088.1774447

Published: 22 March 2010 Publication History

Abstract

Privacy Preserving Distributed OLAP is becoming a critical challenge for next-generation Business Intelligence (BI) scenarios, due to the "natural suitability" of OLAP in analyzing distributed massive BI repositories in a multidimensional and multigranularity manner. In particular, in these scenarios XML-formatted BI repositories play a dominant role, due to the wellknow amenities of XML in modeling and representing distributed business data. However, while Privacy Preserving Distributed Data Mining has been widely investigated, very few efforts have focused on the problem of effectively and efficiently supporting privacy preserving OLAP over distributed collections of XML documents. In order to fulfill this gap, we propose a novel Secure Multiparty Computation (SMC)-based privacy preserving OLAP framework for distributed collections of XML documents. The framework has many novel features ranging from nice theoretical properties to an effective and efficient protocol. The efficiency of our approach has been validated by an experimental evaluation over distributed collections of synthetic XML documents.

References

[1]

R. Agrawal et al., "Privacy-Preserving Data Mining", ACM SIGMOD, 2000, pp. 439--450.

Digital Library

[2]

R. Agrawal et al., "Privacy-Preserving OLAP", ACM SIGMOD, 2005, pp. 251--262.

Digital Library

[3]

K. Beyer et al., "Extending XQuery for Analytics", ACM SIGMOD, 2005, pp. 503--514.

Digital Library

[4]

B. K. Bhargava, "Security in Data Warehousing", DaWaK, LNCS Vol. 1874, 2000, pp. 287--289.

Digital Library

[5]

R. R. Bordawekar et al., "Analytical Processing of XML Documents: Opportunities and Challenges", SIGMOD Record 34(2), 2005, pp. 27--32.

Digital Library

[6]

S. Chaudhuri et al., "An Overview of Data Warehousing and OLAP Technology", SIGMOD Record 26(1), 1997, pp. 65--74.

Digital Library

[7]

C. Clifton et al., "Tools for Privacy Preserving Distributed Data Mining", SIGKDD Explorations 4(2), 2002, pp. 28--34.

Digital Library

[8]

E. F. Codd et al., "Providing OLAP to User-Analysts: An IT Mandate", E. F. Codd & Ass. Tech. Rep., 1993.

[9]

A. Cuzzocrea, "Cubing Algorithms for XML Data", IEEE FlexDBIST, 2009, pp. 407--411.

Digital Library

[10]

A. Cuzzocrea et al., "Privacy Preserving OLAP and OLAP Security", J. Wang (ed.), "Encyclopedia of Data Warehousing and Mining", 2nd edition, IGI Global, 2009, pp. 1575--1581.

[11]

A. Cuzzocrea et al., "A Robust Sampling-based Framework for Privacy Preserving OLAP", DaWaK, LNCS Vol. 5182, 2008, pp. 97--114.

Digital Library

[12]

A. Cuzzocrea et al., "Semantics-aware Advanced OLAP Visualization of Multidimensional Data Cubes", International Journal of Data Warehousing and Mining 3(4), 2007, pp. 1--30.

[13]

P. Drineas et al., "Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition", SIAM Journal on Computing 36(1), 2006, pp. 184--206.

Digital Library

[14]

P. Drineas et al., "Computing Sketches of Matrices Efficiently & (Privacy Preserving) Data Mining", DIMACS PPDM, 2004.

[15]

M. Fang et al., "Computing Iceberg Queries Efficiently", VLDB, 1998, pp. 299--310.

Digital Library

[16]

F. Furfaro et al., "A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-Dimensional Data", SSDBM, LNCS Vol. 5069, 2008, pp. 114--130.

Digital Library

[17]

V. Gaede et al., "Multidimensional Access Methods", ACM Computing Surveys 30(2), 1998, pp. 170--231.

Digital Library

[18]

C. Gokhale et al., "Complex Group-By Queries for XML", IEEE ICDE, 2007, pp. 646--655.

[19]

O. Goldreich, Foundations of Cryptography, Volume II: Basic Applications, Cambridge University Press, 2004.

Digital Library

[20]

G. H. Golub et al., Matrix Computations, Johns Hopkins University Press, 1989.

[21]

J. Gray et al., "Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals", Data Mining and Knowledge Discovery 1(1), 1997, pp. 29--53.

Digital Library

[22]

J. Han et al., "Efficient Computation of Iceberg Cubes with Complex Measures", ACM SIGMOD, 2001, pp. 1--12.

Digital Library

[23]

W. He et al., "PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks", IEEE INFOCOM, 2007, pp. 2045--2053.

Digital Library

[24]

C.-T. Ho et al., "Range Queries in OLAP Data Cubes", ACM SIGMOD, 1997, pp. 73--88.

Digital Library

[25]

M. Hua et al., "FMC: An Approach for Privacy Preserving OLAP", DaWaK, LNCS Vol. 3589, 2005, pp. 408--417.

Digital Library

[26]

M. Jensen et al., "Specifying OLAP Cubes on XML Data". Journal of Intelligent Information Systems 17(2--3), 2001, pp. 255--280.

Digital Library

[27]

C. Kit et al., "OLAP Query Processing for XML Data in RDBMS", IEEE SWOD, 2007, pp. 7--12.

Digital Library

[28]

L. V. S. Lakshmanan et al., "Quotient Cube: How to Summarize the Semantics of a Data Cube", VLDB, 2002, pp 778--789.

Digital Library

[29]

Microsoft Analysis Services, http://www.microsoft.com/sql/evaluation/bi/bianalysis.asp

[30]

T. Niemi et al., "Constructing an OLAP Cube from Distributed XML Data", ACM DOLAP, 2002, pp. 22--27.

Digital Library

[31]

S. Paparizos et al., "Grouping in XML", EDBT Workshops, 2002, pp. 128--147.

Digital Library

[32]

A. Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 1984.

[33]

G. Pernul et al., "Towards OLAP Security Design -- Survey and Research Issues", ACM DOLAP, 2000, pp. 114--121.

Digital Library

[34]

B. Pinkas, "Cryptographic Techniques for Privacy-Preserving Data Mining", SIGKDD Explorations 4(2), 2002, pp. 12--19.

Digital Library

[35]

S. Y. Sung et al., "Privacy Preservation for Data Cubes", Knowledge and Information Systems 9(1), 2006, pp. 38--61.

Digital Library

[36]

L. Wang et al., "Securing OLAP Data Cubes against Privacy Breaches", IEEE SSP, 2004, pp. 161--175.

[37]

L. Wang et al., "Cardinality-based Inference Control in Data Cubes", Journal of Computer Security 12(5), 2004, pp. 655--692.

Digital Library

[38]

N. Wiwatwattana et al., "X^3: A Cube Operator for XML OLAP", IEEE ICDE, 2007, pp. 916--925.

[39]

D. Xin et al., "Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach", VLDB, 2006, pp. 463--475.

Digital Library

Cited By

Chukkapalli SRanade PMittal SJoshi A(2021)A Privacy Preserving Anomaly Detection Framework for Cooperative Smart Farming Ecosystem2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPSISA52974.2021.00037(340-347)Online publication date: Dec-2021
https://doi.org/10.1109/TPSISA52974.2021.00037
Korzh OSharma AJoaristi MSerra ECuzzocrea A(2020)An Innovative Framework for Supporting Remote Sensing in Image Processing Systems via Deep Transfer Learning2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378306(5098-5107)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378306
Olawoyin ALeung CCuzzocrea A(2020)Preserving Privacy of Temporal Big Data2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378040(4042-4051)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378040
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cover image ACM Conferences

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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SAC'10: The 2010 ACM Symposium on Applied Computing

March 22 - 26, 2010

Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

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Cited By

Chukkapalli SRanade PMittal SJoshi A(2021)A Privacy Preserving Anomaly Detection Framework for Cooperative Smart Farming Ecosystem2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPSISA52974.2021.00037(340-347)Online publication date: Dec-2021
https://doi.org/10.1109/TPSISA52974.2021.00037
Korzh OSharma AJoaristi MSerra ECuzzocrea A(2020)An Innovative Framework for Supporting Remote Sensing in Image Processing Systems via Deep Transfer Learning2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378306(5098-5107)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378306
Olawoyin ALeung CCuzzocrea A(2020)Preserving Privacy of Temporal Big Data2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378040(4042-4051)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378040
Hoi CLeung CTran KCuzzocrea ABochicchio MSimonetti M(2018)Supporting Social Information Discovery from Big Uncertain Social Key-Value Data via Graph-Like MetaphorsCognitive Computing – ICCC 201810.1007/978-3-319-94307-7_8(102-116)Online publication date: 20-Jun-2018
https://doi.org/10.1007/978-3-319-94307-7_8
Yan PJin W(2015)Improving Cross-Document Knowledge Discovery Through Content and Link Analysis of Wikipedia KnowledgeTransactions on Large-Scale Data- and Knowledge-Centered Systems XXI10.1007/978-3-662-47804-2_8(161-184)Online publication date: 17-Jul-2015
https://doi.org/10.1007/978-3-662-47804-2_8
Chen QHsu M(2015)Cut-and-Rewind: Extending Query Engine for Continuous Stream AnalyticsTransactions on Large-Scale Data- and Knowledge-Centered Systems XXI10.1007/978-3-662-47804-2_5(94-114)Online publication date: 17-Jul-2015
https://doi.org/10.1007/978-3-662-47804-2_5
Cuzzocrea APuglisi L(2014)Record Linkage in Data WarehousingEncyclopedia of Information Science and Technology, Third Edition10.4018/978-1-4666-5888-2.ch189(1958-1967)Online publication date: 31-Jul-2014
https://doi.org/10.4018/978-1-4666-5888-2.ch189
Cuzzocrea A(2014)Privacy Preserving OLAP Data CubesEncyclopedia of Business Analytics and Optimization10.4018/978-1-4666-5202-6.ch169(1886-1897)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-5202-6.ch169
Tang YLiu LIyengar ALee KZhang Q(2014)e-PPIProceedings of the 2014 IEEE 34th International Conference on Distributed Computing Systems10.1109/ICDCS.2014.27(186-197)Online publication date: 30-Jun-2014
https://dl.acm.org/doi/10.1109/ICDCS.2014.27
Cuzzocrea ABertino E(2014)A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAPProceedings of the Confederated International Workshops on On the Move to Meaningful Internet Systems: OTM 2014 Workshops - Volume 884210.1007/978-3-662-45550-0_16(117-136)Online publication date: 27-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-662-45550-0_16
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