Efficient and Privacy preserving Approximation of Distributed Statistical Queries

Philip Derbeko, Shlomi Dolev, Ehud Gudes, Jeffrey D. Ullman

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
JournalIEEE Transactions on Big Data
StateAccepted/In press - 1 Jan 2021


  • Approximate Computations
  • Approximation algorithms
  • Differential Privacy
  • Differential privacy
  • Distributed Computations
  • Distributed databases
  • Estimation
  • Heuristic algorithms
  • Law enforcement
  • Protocols


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