CAREER: Towards Privacy and Confidentiality Preserving Databases

Many databases from government, commercial and non-profit organizations maintain a huge amount of data on sensitive or confidential information such as income and medical records. As a result, protecting the privacy and confidentiality of such databases is of primary concern. In this project, we focus on quantifying and evaluating the tradeoffs between the data utility and the disclosure risk on applications of various perturbation techniques in practice. We expect to provide a prototype system which can fully conduct disclosure analysis using both model based and randomization based approaches to satisfy users' complex privacy and confidentiality specifications.

People

• Xintao Wu (PI)
• Xiaowei Ying (Ph.D. Graduate)
• Jun Zhu (Ph.D. student)
• Ling Guo  (Ph.D. Graduate)
• Songtao Guo (Ph.D. Graduate)

Paper

• Reconstruction from Randomized Graph via Low Rank Approximation.
  Leting Wu, Xiaowei Ying, and Xintao Wu.
  In Proceedings of the 10th SIAM Conference on Data Mining(SDM), Columbus, Ohio, April 29-May 1, 2010.
  PDF     Slides
• Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters.
  Ling Guo and Xintao Wu.
  Transaction on Data Privacy, 2(3):185-205, Oct 2009. 
  PDF    
• Graph Generation with Prescribed Feature Constraints.
  Xiaowei Ying and Xintao Wu.
  In Proceedings of the 9th SIAM Conference on Data Mining(SDM), Sparks, Nevada, April 30-May 2, 2009.
  PDF     Slides
• On Link Privacy in Randomizing Social Networks.
  Xiaowei Ying and Xintao Wu.
  Technical Report, UNC Charlotte, Sept 23, 2008.
  PDF     Slides
• On Addressing Accuracy Concerns in Privacy and Preserving Association Rule Mining.
  Ling Guo, Songtao Guo, and Xintao Wu.
  In Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Osaka, Japan, May 2008.
  PDF     Slides
• Determining Error Bounds for Spectral Filtering Based Reconstruction Methods in Privacy Preserving Data Mining.
  Ling Guo, Songtao Guo, and Xintao Wu.
  Journal of Knowledge and Information System 17(2):217-240, 2008.
  PDF    
• Protecting Business Intelligence and Customer Privacy while Outsourcing Data Mining Tasks.
  Ling Qiu, Yingjiu Li, and Xintao Wu.
  Journal of Knowledge and Information System 17(1):99-120, 2008.
  PDF
• Privacy Preserving Market Basket Data Analysis.
  Ling Guo, Songtao Guo, and Xintao Wu.
  In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland, Sept 2007.
  PDF     Slides
• Deriving Private Information from Arbitrarily Projected Data.
  Songtao Guo and Xintao Wu.
  In Proceedings of the 11th Pacific-Asia Conference on  Knowledge Discovery and Data Mining (PAKDD), Nanjing, China, May 2007.
  PDF     Slides        PDF (technical report version)
• On the Lower Bound of Reconstruction Error for Spectral Filtering based Privacy Preserving Data Mining.
  Songtao Guo, Xintao Wu, and Yingjiu Li.
  In Proceedings of the 10th European Conference on  Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, Sept 2006.
  PDF     Slides
• On the Use of Spectral Filtering for Privacy Preserving Data Mining.
  Songtao Guo and Xintao Wu.
  In Proceedings of the 21st ACM Symposium on Applied Computing, Dijon, France, April 2006.
  PDF     Slides
• Towards Value Disclosure Analysis in Modeling General Databases.
  Xintao Wu, Songtao Guo, Yingjiu Li.
  In Proceedings of the 21st ACM Symposium on Applied Computing, Dijon, France, April 2006.
  PDF     Slides
• Deriving Private Information from Perturbed Data using IQR based Approach.
  Songtao Guo, Xintao Wu, Yingjiu Li.
  In Proceedings of the 2nd International Workshop on Privacy Data Management, Atlanta, April 2006.
  PDF     Slides   

Misc

• A Tutorial of Privacy-Preservation of Graphs and Social Networks.
  Xintao Wu and Xiaowei Ying.
  Tutorial at PAKDD11.
  Slides  
• Randomization based Privacy Preserving Data Mining.
  Xintao Wu.
  Tutorial at WAIM06 and PKDD/ECML06.
  Slides   Bib

Acknowledgements

This material is based upon work supported by National Science Foundation under CAREER Award IIS-0546027. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Data Privacy Lab  
University of North Carolina at Charlotte