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- W2963848253 abstract "In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations. In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach." @default.
- W2963848253 created "2019-07-30" @default.
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- W2963848253 date "2017-12-01" @default.
- W2963848253 modified "2023-10-04" @default.
- W2963848253 title "Efficient and private approximations of distributed databases calculations" @default.
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- W2963848253 doi "https://doi.org/10.1109/bigdata.2017.8258489" @default.
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