Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Disclosure Risks of Distance Preserving Data Transformations
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Deriving private information from arbitrarily projected data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
An attacker's view of distance preserving maps for privacy preserving data mining
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Hi-index | 0.00 |
Outsourcing data to external parties for analysis is risky as the privacy of confidential variables can be easily violated. To eliminate this threat, the data values of these variables should be perturbed before releasing the data. However, the perturbation itself may significantly change the underlying properties of the data, affecting the analysis results. What is required is a subtle transformation to generate perturbed data that maintains, as much as possible, the statistical properties and effectiveness (i.e. the utility) of the original data whilst preserving the privacy. We examine privacy-preserving transformations in the context of data clustering. In particular, this paper demonstrates how nonmetric multidimensional scaling (MDS) can be profitably used as a perturbation tool and how the perturbed data can be effectively used in clustering analysis without compromising privacy or utility. We apply the proposed technique to real datasets and compare the results, which were, in some circumstances, exactly the same as those obtained from the original data.