skip to main content
10.1145/1871940.1871960acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Balancing accuracy and privacy of OLAP aggregations on data cubes

Published: 30 October 2010 Publication History

Abstract

In this paper we propose an innovative framework based on flexible sampling-based data cube compression techniques for computing privacy preserving OLAP aggregations on data cubes while allowing approximate answers to be efficiently evaluated over such aggregations. In our proposal, this scenario is accomplished by means of the so-called accuracy/privacy contract, which determines how OLAP aggregations must be accessed throughout balancing accuracy of approximate answers and privacy of sensitive ranges of multidimensional data.

References

[1]
S. Agarwal et al., "On the Computation of Multidimensional Aggregates", VLDB, 506--521, 1996.
[2]
R. Agrawal et al., "Privacy-Preserving OLAP", ACM SIGMOD, 251--262, 2005.
[3]
A. Cuzzocrea, "Overcoming Limitations of Approximate Query Answering in OLAP", IEEE IDEAS, 200--209, 2005.
[4]
A. Cuzzocrea, "Accuracy Control in Compressed Multidimensional Data Cubes for Quality of Answer-based OLAP Tools", IEEE SSDBM, 301--310, 2006.
[5]
A. Cuzzocrea, "Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation", Data & Knowledge Engineering, 56(2), 85--121, 2006.
[6]
J. Han et al., "Efficient Computation of Iceberg Cubes with Complex Measures," ACM SIGMOD, 1--12, 2001.
[7]
M. Hua et al., "FMC: An Approach for Privacy Preserving OLAP", DaWaK, 408--417, 2005.
[8]
A. Machanavajjhala et al., "L-diversity: Privacy beyond k-Anonymity", ACM Trans. on Knowledge Discovery from Data, 1(1), art. no. 3, 2007.
[9]
S.Y. Sung et al., "Privacy Preservation for Data Cubes", Knowledge and Information Systems, 9(1), 38--61, 2006.
[10]
L. Sweeney, "k-Anonymity: A Model for Protecting Privacy", International Journal on Uncertainty Fuzziness and Knowledge-based Systems, 10(5), 557--570, 2002.
[11]
L. Wang et al., "Securing OLAP Data Cubes against Privacy Breaches", IEEE SSP, 161--175, 2004.
[12]
L. Wang et al., "Cardinality-based Inference Control in Data Cubes", Journal of Computer Security, 12(5), 655--692, 2004.
[13]
N. Zhang et al., "Cardinality-based Inference Control in OLAP Systems: An Information Theoretic Approach", ACM DOLAP, 59--64, 2004.

Cited By

View all
  • (2023)A Theoretical Framework for Supporting Clustering Validation via Non-Negative-Matrix-Factorization Trace Sequences Over Probabilistic Spaces2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361428(1080-1087)Online publication date: 14-Nov-2023
  • (2023)Big OLAP Data Cube Compression Algorithms in Column-Oriented Cloud/Edge Data Infrastructures2023 IEEE Ninth Multimedia Big Data (BigMM)10.1109/BigMM59094.2023.00020(1-2)Online publication date: 11-Dec-2023
  • (2023)F-TBDA: A Frequency-Based Temporal Big Data Analytics Technique for Mining and Analyzing Quality-Of-Life Indicators of Cancer Patients2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386767(5197-5205)Online publication date: 15-Dec-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DOLAP '10: Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
October 2010
112 pages
ISBN:9781450303835
DOI:10.1145/1871940
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2010

Permissions

Request permissions for this article.

Check for updates

Author Tag

  1. privacy preserving olap; accuracy/privacy constraints over olap data cubes.

Qualifiers

  • Research-article

Conference

CIKM '10

Acceptance Rates

Overall Acceptance Rate 29 of 79 submissions, 37%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)A Theoretical Framework for Supporting Clustering Validation via Non-Negative-Matrix-Factorization Trace Sequences Over Probabilistic Spaces2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361428(1080-1087)Online publication date: 14-Nov-2023
  • (2023)Big OLAP Data Cube Compression Algorithms in Column-Oriented Cloud/Edge Data Infrastructures2023 IEEE Ninth Multimedia Big Data (BigMM)10.1109/BigMM59094.2023.00020(1-2)Online publication date: 11-Dec-2023
  • (2023)F-TBDA: A Frequency-Based Temporal Big Data Analytics Technique for Mining and Analyzing Quality-Of-Life Indicators of Cancer Patients2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386767(5197-5205)Online publication date: 15-Dec-2023
  • (2023)Towards Big Data Analytics over Mobile User Data using Machine Learning2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386730(5365-5371)Online publication date: 15-Dec-2023
  • (2018)Scalable Privacy-Preserving Big Data Management and AnalyticsProceedings of the 2018 2nd International Conference on Cloud and Big Data Computing10.1145/3264560.3266429(52-56)Online publication date: 3-Aug-2018
  • (2018)A General Overview of Privacy-Preserving Big Data Management and Analytics Models, Methods and Techniques in Specific Domains: Static and Dynamic Distributed Environments2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8621882(5093-5100)Online publication date: Dec-2018
  • (2018)Advanced pattern recognition from complex environmentsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2661-022:14(4763-4778)Online publication date: 1-Jul-2018
  • (2017)Big data challenges: Prioritizing by decision-making process using Analytic Network Process techniqueMultimedia Tools and Applications10.1007/s11042-017-5161-4Online publication date: 24-Sep-2017
  • (2017)From Star Schemas to Big Data: 20 $$+$$ Years of Data Warehouse ResearchA Comprehensive Guide Through the Italian Database Research Over the Last 25 Years10.1007/978-3-319-61893-7_6(93-107)Online publication date: 31-May-2017
  • (2016)Evaluating the impact of k-anonymization on the inference of interaction networksTransactions on Data Privacy10.5555/2993210.29932139:1(49-72)Online publication date: 1-Apr-2016
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media