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A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAP

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On the Move to Meaningful Internet Systems: OTM 2014 Workshops (OTM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8842))

Abstract

This paper complements the privacy preserving distributed OLAP framework proposed by us in a previous work by introducing four major theoretical properties that extend models and algorithms presented in the previous work, where the experimental validation of the framework has also been reported. Particularly, our framework makes use of the CUR matrix decomposition technique as the elementary component for computing privacy preserving two-dimensional OLAP views effectively and efficiently. Here, we investigate theoretical properties of the CUR decomposition method, and identify four theoretical extensions of this method, which, according to our vision, may result in benefits for a wide spectrum of aspects in the context of privacy preserving distributed OLAP, such as privacy preserving knowledge fruition schemes and query optimization. In addition to this, we also provide a widespread experimental analysis of the framework, which fully confirms to us the major practical achievements, in terms of both efficacy and efficiency, due to our framework.

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References

  1. Agrawal, S., Haritsa, J.R., Prakash, B.A.: FRAPP: A Framework for High-Accuracy Privacy-Preserving Mining. Data Mining and Knowledge Discovery 18(1), 101–139 (2009)

    Article  MathSciNet  Google Scholar 

  2. Agrawal, R., Srikant, R., Thomas, D.: Privacy-Preserving OLAP. Proc. of SIGMOD, 251–262 (2005)

    Google Scholar 

  3. Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release. In: Proc. of PODS, pp. 273–282 (2007)

    Google Scholar 

  4. 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 

  5. Clifton, C., Kantarcioglu, M., Lin, X., Vaidya, J., Zhu, M.: Tools for Privacy Preserving Distributed Data Mining. SIGKDD Explorations 4(2), 28–34 (2002)

    Article  Google Scholar 

  6. Colliat, G.: OLAP, Relational, and Multidimensional Database Systems. SIGMOD Record 25(3), 64–69 (1996)

    Article  Google Scholar 

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

    Google Scholar 

  8. Cuzzocrea, A.: Privacy Preserving OLAP: Models, Issues, Algorithms. In: Proc. of MIPRO, pp. 1538–1543 (2011)

    Google Scholar 

  9. 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 

  10. 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 

  11. 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 

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

    Google Scholar 

  13. Drineas, P., Kannan, R., Mahoney, M.W.: Computing Sketches of Matrices Efficiently and Privacy Preserving Data Mining. In: Proc. of DIMACS PPDM (2004), http://dimacs.rutgers.edu/Workshops/Privacy/

  14. Drineas, P., Kannan, R., Mahoney, M.W.: Fast Monte Carlo algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition. SIAM Journal on Computing 36(1), 184–206 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Dwork, C.: Differential privacy: A survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: 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 

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

    Google Scholar 

  18. Han, J., Pei, J., Dong, G., Wang, K.: Efficient Computation of Iceberg Cubes with Complex Measures. Proc. of SIGMOD, 1–12 (2001)

    Google Scholar 

  19. He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.: PDA: Privacy-Preserving Data Aggregation for Information Collection. ACM Transactions on Sensor Networks 8(1), art. 6 (2011)

    Google Scholar 

  20. 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 

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

    Google Scholar 

  22. 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 

  23. Li, F., Luo, B., Liu, P.: Secure and Privacy-Preserving Information Aggregation for Smart Grids. International Journal of Security and Networks 6(1), 28–39 (2011)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  25. Liu, Y., Sung, S.Y., Xiong, H.: A Cubic-Wise Balance Approach for Privacy Preservation in Data Cubes. Information Sciences 176(9), 1215–1240 (2006)

    Article  MATH  Google Scholar 

  26. Mohammed, N., Fung, B.C.M., Hung, P.C.K., Lee, C.-K.: Centralized and Distributed Anonymization for High-Dimensional Healthcare Data. ACM Transactions on Knowledge Discovery from Data 4(4), art. 18 (2010)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  29. Sung, S.Y., Liu, Y., Xiong, H., Ng, P.A.: Privacy Preservation for Data Cubes. Knowledge and Information Systems 9(1), 38–61 (2006)

    Article  Google Scholar 

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

    Google Scholar 

  31. Tong, Y., Sun, G., Zhang, P., Tang, S.: Privacy-Preserving OLAP based on Output Perturbation Across Multiple Sites. In: Proc. of PST, p. 46 (2006)

    Google Scholar 

  32. Wang, L., Jajodia, S., Wijesekera, D.: Securing OLAP Data Cubes against Privacy Breaches. In: Proc. of SP, pp. 161–175 (2004)

    Google Scholar 

  33. Wang, L., Wijesekera, D., Jajodia, S.: Cardinality-based Inference Control in Data Cubes. Journal of Computer Security 12(5), 655–692 (2004)

    Google Scholar 

  34. Zhang, N., Zhao, W., Chen, J.: Cardinality-based Inference Control in OLAP Systems: An Information Theoretic Approach. In: Proc. of DOLAP, pp. 59–64 (2004)

    Google Scholar 

  35. Zipf, G.K.: Human Behaviour and the Principle of Least Effort: an Introduction to Human Ecology. Addison-Wesley (1949)

    Google Scholar 

  36. Schmidt, A., Waas, F., Kersten, M.L., Carey, M.J., Manolescu, I., Busse, R.: XMark: A Benchmark for XML Data Management. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 974–985 (2002)

    Google Scholar 

  37. Transaction Processing Performance Council (2004), http://www.tpc.org/tpch/default.asp

  38. Yao, B.B., Özsu, M.T., Khandelwal, N.: XBench Benchmark and Performance Testing of XML DBMSs. In: Proceedings of the 20th IEEE International Conference on Data Engineering, pp. 621–632 (2004)

    Google Scholar 

  39. University of Pennsylvania, The Penn Treebank Project (2002), http://www.cis.upenn.edu/~treebank/

  40. Swiss Institute of Bioinformatics, Swiss-Prot Protein Knowledgebase (2005), http://www.expasy.ch/sprot/

  41. GSFC/NASA XML Project, NASA (2003), http://xml.gsfc.nasa.gov

  42. Cuzzocrea, A., Bertino, E., Saccà, D.: Towards A Theory for Privacy Preserving Distributed OLAP. In: Proceedings of the EDBT/ICDT Workshops, pp. 221–226 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ICAR-CNR and University of Calabria, Italy

    Alfredo Cuzzocrea

  2. CERIAS and Purdue University, IN, USA

    Elisa Bertino

Authors
  1. Alfredo Cuzzocrea
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  2. Elisa Bertino
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Editors and Affiliations

  1. TU Graz, Rechbauerstraße 12, 8010, Graz, Austria

    Robert Meersman

  2. CRAN, University of Lorraine, Campus Sciences, BP 70239, 54506, Vandoevre-les-Nancy, France

    Hervé Panetto

  3. Department of Software Engineering, Atilim Univeristy, Kızılcaşar Mh Mh, 06830, Ankara, Turkey

    Alok Mishra

  4. Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100, Murcia, Spain

    Rafael Valencia-García

  5. Dep. of Informatics Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    António Lucas Soares

  6. SIGMA / LIG, Joseph Fourier University, 220 Rue de la Chimie, BP 53, 38041, Grenoble Cedex 9, France

    Ioana Ciuciu

  7. Institute for Research on Population and Social, National Research Council, Policies, Via Palestro 32, 00185, Rome, Italy

    Fernando Ferri

  8. Dep. Business Informatics, Communications Engineering, Johannes Kepler University, Freistaedterstrasse 315, 4040, Linz, Austria

    Georg Weichhart

  9. STINA Business Solutions GmbH, Schottenfeldgasse 63/2, 1070, Wien, Austria

    Thomas Moser

  10. SAP Research, 805 avenue du Dr Donat, 06259, Sophia-Antipolis, Mougins, France

    Michele Bezzi

  11. Department of Computing, Hong Kong Polytechnic University, PQ806, Mong Man Wai Building, Hong Kong,, China

    Henry Chan

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Cuzzocrea, A., Bertino, E. (2014). A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAP. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2014 Workshops. OTM 2014. Lecture Notes in Computer Science, vol 8842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45550-0_16

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  • DOI: https://doi.org/10.1007/978-3-662-45550-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45549-4

  • Online ISBN: 978-3-662-45550-0

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