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In recent years, an increasing amount of data is collected in different andoften, not cooperative, databases. The problem of privacy-preserving,distributed calculations over separated databases and, a relative to it, issueof private data release were intensively investigated. However, despite aconsiderable progress, computational complexity, due to an increasing size ofdata, remains a limiting factor in real-world deployments, especially in caseof privacy-preserving computations.  In this paper, we present a general method for trade off between performanceand accuracy of distributed calculations by performing data sampling. Samplingwas 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 onapproximation of intersection set both without and with privacy-preservingmechanism. An analysis of the bound on error as a function of the sample sizeis discussed and heuristic algorithm is suggested to further improve theperformance. The algorithms were implemented and experimental results confirmthe validity of the approach.

Efficient and Private Approximations of Distributed Databases Calculations