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Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP Algorithms

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Book cover Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

Actually, a lot of attention focusing on the problem of computing privacy-preserving OLAP cubes effectively and efficiently arises. State-of-the-art proposals rather focus on an algorithmic vision of the problem, and neglect relevant theoretical aspects the investigated problem introduces naturally. In order to fulfill this gap, in this paper we provide algorithms for supporting privacy-preserving OLAP in distributed environments, based on the well-known CUR matrix decomposition method, enriched by some relevant theory-inspired optimizations that look at the intrinsic nature of the investigated problem in order to gain significant benefits, at both the (privacy-preserving) cube computation level and the (privacy-preserving) cube delivery level.

The work reported in this paper has been partially supported by the US National Science Foundation under grants CNS-1111512 and CNS-1016722.

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References

  1. Agrawal, R., et al.: Privacy-Preserving OLAP. In: Proc. of SIGMOD, pp. 251–262 (2005)

    Google Scholar 

  2. Chan, A.C.-F., Castelluccia, C.: A Security Framework for Privacy-Preserving Data Aggregation in Wireless Sensor Networks. ACM Transactions on Sensor Networks 7(4), art. 29 (2011)

    Google Scholar 

  3. Clifton, C., et al.: Tools for Privacy Preserving Distributed Data Mining. SIGKDD Explorations 4(2), 28–34 (2002)

    Article  MathSciNet  Google Scholar 

  4. Cuzzocrea, A.: Accuracy Control in Compressed Multidimensional Data Cubes for Quality of Answer-based OLAP Tools. In: Proc. of SSDBM 2006, pp. 301–310 (2006)

    Google Scholar 

  5. Cuzzocrea, A., Bertino, E.: A Secure Multiparty Computation Privacy Preserving OLAP Framework over Distributed XML Data. In: Proc. of SAC, pp. 1666–1673 (2010)

    Google Scholar 

  6. Cuzzocrea, A., Russo, V.: Privacy Preserving OLAP and OLAP Security. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, 2nd edn., pp. 1575–1581. IGI Global (2009)

    Google Scholar 

  7. Cuzzocrea, A., Russo, V., Saccà, D.: A Robust Sampling-based Framework for Privacy Preserving OLAP. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 97–114. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Cuzzocrea, A., Saccà, D.: Balancing Accuracy and Privacy of OLAP Aggregations on Data Cubes. In: Proc. of DOLAP, pp. 93–98 (2010)

    Google Scholar 

  9. Drineas, P., et al.: Computing Sketches of Matrices Efficiently and Privacy Preserving Data Mining. In: Proc. of DIMACS PPDM (2004)

    Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  11. Cuzzocrea, A., Bertino, E.: Further Theoretical Contributions to a Privacy Preserving Distributed OLAP Framework. In: Proc. of COMPSAC, pp. 234–239 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  13. Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins University Press (1989)

    Google Scholar 

  14. He, W., et al.: PDA: Privacy-Preserving Data Aggregation for Information Collection. ACM Transactions on Sensor Networks 8(1), art. 6 (2011)

    Google Scholar 

  15. Hua, M., Zhang, S., Wang, W., Zhou, H., Shi, B.-L.: FMC: An Approach for Privacy Preserving OLAP. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 408–417. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Jiang, W., Clifton, C.: A Secure Distributed Framework for Achieving k-Anonymity. Very Large Data Bases Journal 15(4), 316–333 (2006)

    Article  Google Scholar 

  17. Jurczyk, P., Xiong, L.: Distributed Anonymization: Achieving Privacy for Both Data Subjects and Data Providers. In: Gudes, E., Vaidya, J. (eds.) Data and Applications Security XXIII. LNCS, vol. 5645, pp. 191–207. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Li, F., et al.: Secure and Privacy-Preserving Information Aggregation for Smart Grids. International Journal of Security and Networks 6(1), 28–39 (2011)

    Article  Google Scholar 

  19. Lin, X., et al.: MDPA: Multidimensional Privacy-Preserving Aggregation Scheme for Wireless Sensor Networks. Wireless Communications and Mobile Computing 10(6), 843–856 (2010)

    Google Scholar 

  20. Liu, Y., et al.: A Cubic-Wise Balance Approach for Privacy Preservation in Data Cubes. Information Sciences 176(9), 1215–1240 (2006)

    Article  MATH  Google Scholar 

  21. Mohammed, N., et al.: Centralized and Distributed Anonymization for High-Dimensional Healthcare Data. ACM Transactions on Knowledge Discovery from Data 4(4), art. 18 (2010)

    Google Scholar 

  22. Papoulis, A.: Probability, Random Variables, and Stochastic Processes. McGraw-Hill (1984)

    Google Scholar 

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

    Article  Google Scholar 

  24. Sung, S.Y., et al.: Privacy Preservation for Data Cubes. Knowledge and Information Systems 9(1), 38–61 (2006)

    Article  Google Scholar 

  25. Thompson, S.K., Seber, G.A.F.: Adaptive Sampling. John Wiley & Sons (1996)

    Google Scholar 

  26. Tong, Y., et al.: Privacy-Preserving OLAP based on Output Perturbation Across Multiple Sites. In: Proc. of PST, p. 46 (2006)

    Google Scholar 

  27. Wang, L., et al.: Securing OLAP Data Cubes against Privacy Breaches. In: Proc. of SP, pp. 161–175 (2004)

    Google Scholar 

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

    Google Scholar 

  29. Zhang, N., et al.: Cardinality-based Inference Control in OLAP Systems: An Information Theoretic Approach. In: Proc. of DOLAP, pp. 59–64 (2004)

    Google Scholar 

  30. Borrajo, M.L., et al.: Hybrid Neural Intelligent System to Predict Business Failure in Small-To-Medium-Size Enterprises. International Journal of Neural Systems 21(4), 277–296 (2011)

    Article  Google Scholar 

  31. Abraham, A.: Special Issue: Hybrid Approaches for Approximate Reasoning. Journal of Intelligent and Fuzzy Systems 23(2-3), 41–42 (2012)

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. ICAR-CNR and University of Calabria, Cosenza, Italy

    Alfredo Cuzzocrea & Elisa Bertino

  2. CERIAS and Purdue University, West Lafayette, IN, USA

    Alfredo Cuzzocrea & Elisa Bertino

Authors
  1. Alfredo Cuzzocrea
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  2. Elisa Bertino
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Cuzzocrea, A., Bertino, E. (2014). Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP Algorithms. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_39

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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