Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas, and François Taïani

Fingerprinting Big Data: The Case of KNN Graph Construction (short paper)

35th IEEE International Conference on Data Engineering (ICDE 2019), Macao, China, April, pp. 1738-1741, IEEE, 2019 (4p.)

We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that fingerprinting can drastically accelerate a large range of existing KNN algorithms, while efficiently obfuscating the original data, with little to no overhead. Our extensive evaluation of the resulting approach (dubbed GoldFinger) on several realistic datasets shows that our approach delivers speedups of up to 78.9%

complete document

doi: http://doi.org/10.1109/ICDE.2019.00186 (publisher's link)


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