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 was intensively investigated. However, despite a considerable progress, computational complexity and consequently, the performance of the computations, due to an increasing size of data, remains a limiting factor in real-world deployments. Especially in the 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 in the past 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 an approximation of intersection set both with and without a privacy-preserving mechanism. An analysis of the bound on the error as a function of the sample size is discussed and a heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.
Original language | English |
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Pages (from-to) | 1399-1413 |
Number of pages | 15 |
Journal | IEEE Transactions on Big Data |
Volume | 8 |
Issue number | 5 |
DOIs | |
State | Published - 1 Oct 2022 |
Keywords
- Differential privacy
- approximate computations
- distributed computations
ASJC Scopus subject areas
- Information Systems
- Information Systems and Management