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- W1534415412 abstract "In many privacy preserving applications, specific variables are required to be disturbed simultaneously in order to guarantee correlations among them. Multivariate Equi-Depth Swapping (MEDS) is a natural solution in such cases, since it provides uniform privacy protection for each data tuple. However, this approach performs ineffectively not only in computational complexity (basically O(n 3) for n data tuples), but in data utility for distance-based data analysis. This paper discusses the utilisation of Multivariate Equi-Width Swapping (MEWS) to enhance the utility preservation for such cases. With extensive theoretical analysis and experimental results, we show that, MEWS can achieve a similar performance in privacy preservation to that of MEDS and has only O(n) computational complexity." @default.
- W1534415412 created "2016-06-24" @default.
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- W1534415412 date "2010-01-01" @default.
- W1534415412 modified "2023-09-22" @default.
- W1534415412 title "Multivariate Equi-width Data Swapping for Private Data Publication" @default.
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- W1534415412 doi "https://doi.org/10.1007/978-3-642-13657-3_24" @default.
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