Journal
IEEE TRANSACTIONS ON BIG DATA
Volume 8, Issue 5, Pages 1399-1413Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBDATA.2021.3052516
Keywords
Distributed databases; Protocols; Differential privacy; Estimation; Approximation algorithms; Heuristic algorithms; Law enforcement; Differential privacy; distributed computations; approximate computations
Funding
- Ben-Gurion university Cyber center on Privacy in social networks
- Rita Altura Trust Chair in Computer Sciences
- Frankel center for computer science
- Ministry of Science, Technology and Space, Israel
- National Science Council (NSC) of Taiwan
- Ministry of Foreign Affairs, Italy
- Ministry of Science, Technology and Space, Infrastructure Research in the Field of Advanced Computing and the Cyber Security
- Israel National Cyber Bureau
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In recent years, the increasing amount of data collected from different and often non-cooperative databases has posed challenges for privacy-preserving distributed calculations. This paper proposes a sampling method to improve computational performance and discusses an analysis of error bounds. Experimental results confirm the validity of the approach.
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.
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