Solving mixed integer nonlinear programs by outer approximation
Mathematical Programming: Series A and B
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
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
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Information Retrieval
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Multi-Criteria Capital Budgeting Using FLIP
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
IEEE Transactions on Knowledge and Data Engineering
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Dimensionality Reduction and Similarity Computation by Inner-Product Approximations
IEEE Transactions on Knowledge and Data Engineering
A privacy preserving technique for distance-based classification with worst case privacy guarantees
Data & Knowledge Engineering
Distributed and Parallel Databases
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With the explosive growth of data and its distributed sources, there are increasing needs for secure cooperative data analysis. The issue of data reduction to decrease communication overheads and the issue of preservation of privacy of the shared data are becoming important. However, existing privacy preserving techniques do not work well for distance-based mining because they do not preserve distances. Besides, most of them either do not reduce data or are tied to very specific mining algorithms. Using the unitarity and energy compaction property of Fourier transforms, this paper proposes a novel framework to preserve privacy and reduce data size, yet preserve Euclidian distances. A fuzzy programming approach for selection of Fourier coefficients is proposed to optimise the objective of preserving Euclidean distances and obtaining privacy and data reduction through coefficient suppression. Experiments demonstrate the superiority of the proposed approach over the existing ones.