Privacy-preserving data mashup

ABSTRACT

Mashup is a web technology that combines information from more than one source into a single web application. This technique provides a new platform for different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In this paper, we study and resolve a real-life privacy problem in a data mashup application for the financial industry in Sweden, and propose a privacy-preserving data mashup (PPMashup) algorithm to securely integrate private data from different data providers, whereas the integrated data still retains the essential information for supporting general data exploration or a specific data mining task, such as classification analysis. Experiments on real-life data suggest that our proposed method is effective for simultaneously preserving both privacy and information usefulness, and is scalable for handling large volume of data.

REFERENCES

• 1: J. M. Abowd and J. Lane. New approaches to confidentiality protection: Synthetic data, remote access and research data centers. In Proc. of Privacy in Statistical Databases: CASC Project International Workshop (PSD 2004), pages 282--289, Barcelona, Spain, June 2004.
• 2: Rakesh Agrawal , Alexandre Evfimievski , Ramakrishnan Srikant, Information sharing across private databases, Proceedings of the 2003 ACM SIGMOD international conference on Management of data, June 09-12, 2003, San Diego, California [doi>10.1145/872757.872771]
• 3: Rakesh Agrawal , Ramakrishnan Srikant, Privacy-preserving data mining, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.439-450, May 15-18, 2000, Dallas, Texas, United States
• 4: Roberto J. Bayardo , Rakesh Agrawal, Data Privacy through Optimal k-Anonymization, Proceedings of the 21st International Conference on Data Engineering, p.217-228, April 05-08, 2005 [doi>10.1109/ICDE.2005.42]
• 5: Chris Clifton , Murat Kantarcioglu , Jaideep Vaidya , Xiaodong Lin , Michael Y. Zhu, Tools for privacy preserving distributed data mining, ACM SIGKDD Explorations Newsletter, v.4 n.2, p.28-34, December 2002 [doi>10.1145/772862.772867]
• 6: U. Dayal and H. Y. Hwang. View definition and generalization for database integration in a multidatabase systems. IEEE Transactions on Software Engineering, 10(6):628--645, 1984.
• 7: W. Du, Y. S. Han, and S. Chen. Privacy-preserving multivariate statistical analysis: Linear regression and classification. In Proc. of the 4th SDM, Florida, 2004.
• 8: Wenliang Du , Zhijun Zhan, Building decision tree classifier on private data, Proceedings of the IEEE international conference on Privacy, security and data mining, p.1-8, December 01, 2002, Maebashi City, Japan
• 9: Csilla Farkas , Sushil Jajodia, The inference problem: a survey, ACM SIGKDD Explorations Newsletter, v.4 n.2, p.6-11, December 2002 [doi>10.1145/772862.772864]
• 10: W. A. Fuller. Masking procedures for microdata disclosure limitation. Official Statistics, 9(2):383--406, 1993.
• 11: Benjamin C. M. Fung , Ke Wang , Ada Wai-Chee Fu , Jian Pei, Anonymity for continuous data publishing, Proceedings of the 11th international conference on Extending database technology: Advances in database technology, March 25-29, 2008, Nantes, France [doi>10.1145/1353343.1353378]
• 12: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Top-Down Specialization for Information and Privacy Preservation, Proceedings of the 21st International Conference on Data Engineering, p.205-216, April 05-08, 2005 [doi>10.1109/ICDE.2005.143]
• 13: Benjamin C. M. Fung , Ke Wang , Philip S. Yu, Anonymizing Classification Data for Privacy Preservation, IEEE Transactions on Knowledge and Data Engineering, v.19 n.5, p.711-725, May 2007 [doi>10.1109/TKDE.2007.1015]
• 14: J. Goguen and J. Meseguer. Unwinding and inference control. In Proc. of the IEEE Symposium on Security and Privacy, Oakland, CA, 1984.
• 15: T. Hinke. Inference aggregation detection in database management systems. In Proc. of the IEEE Symposium on Security and Privacy, pages 96--107, Oakland, CA, April 1988.
• 16: T. Hinke, H. Degulach, and A. Chandrasekhar. A fast algorithm for detecting second paths in database inference analysis. Journal of Computer Security, 1995.
• 17: R. D. Hof. Mix, match, and mutate. Business Week, July 2005.
• 18: A. Hundepool and L. Willenborg. μ- and τ-argus: Software for statistical disclosure control. In Proc. of the 3rd International Seminar on Statistical Confidentiality, 1996.
• 19: Vijay S. Iyengar, Transforming data to satisfy privacy constraints, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775089]
• 20: S. Jajodia and C. Meadows. Inference problems in multilevel database management systems. IEEE Information Security: An Integrated Collection of Essays, pages 570--584, 1995.
• 21: W. Jiang and C. Clifton. Privacy-preserving distributed k-anonymity. In Proc. of the 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security, pages 166--177, August 2005.
• 22: Wei Jiang , Chris Clifton, A secure distributed framework for achieving k-anonymity, The VLDB Journal — The International Journal on Very Large Data Bases, v.15 n.4, p.316-333, November 2006 [doi>10.1007/s00778-006-0008-z]
• 23: J. Kim and W. Winkler. Masking microdata files. In Proc. of the Section on Survey Research Methods, pages 114--119, 1995.
• 24: Kristen LeFevre , David J. DeWitt , Raghu Ramakrishnan, Workload-aware anonymization, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150435]
• 25: Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , Muthuramakrishnan Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD), v.1 n.1, p.3-es, March 2007 [doi>10.1145/1217299.1217302]
• 26: J. M. Mateo-Sanz, A. Martínez-Ballesté, and J. Domingo-Ferrer. Fast generation of accurate synthetic microdata. In Proceedings of Privacy in Statistical Databases: CASC Project International Workshop (PSD 2004), pages 298--306, Barcelona, Spain, June 2004.
• 27: Gerome Miklau , Dan Suciu, A formal analysis of information disclosure in data exchange, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France [doi>10.1145/1007568.1007633]
• 28: D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz. UCI repository of machine learning databases, 1998. http://ics.uci.edu/~mlearn/MLRepository.html.
• 29: N. Nisan. Algorithms for selfish agents. In Proceedings of the 16th Symposium on Theoretical Aspects of Computer Science, Trier, Germany, March 1999.
• 30: S. Pohlig and M. Hellman. An improved algorithm for computing logarithms over gf(p) and its cryptographic significance. IEEE Transactions on Information Theory, IT-24:106--110, 1978.
• 31: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 32: P. Samarati, Protecting Respondents' Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, v.13 n.6, p.1010-1027, November 2001 [doi>10.1109/69.971193]
• 33: Pierangela Samarati , Latanya Sweeney, Generalizing data to provide anonymity when disclosing information (abstract), Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, p.188, June 01-04, 1998, Seattle, Washington, United States [doi>10.1145/275487.275508]
• 34: Latanya Sweeney, Achieving k-anonymity privacy protection using generalization and suppression, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.571-588, October 2002 [doi>10.1142/S021848850200165X]
• 35: Jaideep Vaidya , Chris Clifton, Privacy preserving association rule mining in vertically partitioned data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775142]
• 36: Jaideep Vaidya , Chris Clifton, Privacy-preserving k-means clustering over vertically partitioned data, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956776]
• 37: Ke Wang , Benjamin C. M. Fung, Anonymizing sequential releases, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150449]
• 38: Ke Wang , Benjamin C. M. Fung , Philip S. Yu, Handicapping attacker's confidence: an alternative to k-anonymization, Knowledge and Information Systems, v.11 n.3, p.345-368, April 2007 [doi>10.1007/s10115-006-0035-5]
• 39: Gio Wiederhold, Intelligent integration of information, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.434-437, May 25-28, 1993, Washington, D.C., United States
• 40: Raymond Chi-Wing Wong , Jiuyong Li , Ada Wai-Chee Fu , Ke Wang, (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150499]
• 41: Xiaokui Xiao , Yufei Tao, Anatomy: simple and effective privacy preservation, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
• 42: Xiaokui Xiao , Yufei Tao, Personalized privacy preservation, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA [doi>10.1145/1142473.1142500]
• 43: Xiaokui Xiao , Yufei Tao, M-invariance: towards privacy preserving re-publication of dynamic datasets, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247556]
• 44: Zhiqiang Yang , Sheng Zhong , Rebecca N. Wright, Anonymity-preserving data collection, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081909]
• 45: Z. Yang, S. Zhong, and R. N. Wright. Privacy-preserving classification of customer data without loss of accuracy. In Proc. of the 5th SDM, pages 92--102, 2005.
• 46: Andrew C. Yao, Protocols for secure computations, Proceedings of the 23rd Annual Symposium on Foundations of Computer Science, p.160-164, November 03-05, 1982