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Journal Articles IEEE Transactions on Knowledge and Data Engineering Year : 2023

GoldFinger: Fast & Approximate Jaccard for Efficient KNN Graph Constructions

Abstract

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard's index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and show that GoldFinger can drastically accelerate a large range of existing KNN algorithms with little to no overhead. As a side effect, we also show that the compact representation of the data protects users' privacy for free by providing k-anonymity and l-diversity. Our extensive evaluation of the resulting approach on several realistic datasets shows that our approach reduces computation times by up to 78.9% compared to raw data while only incurring a negligible to moderate loss in terms of KNN quality. We also show that GoldFinger can be applied to KNN queries (a widely-used search technique) and delivers speedups of up to ×3.55 over one of the most efficient approaches to this problem.
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Dates and versions

hal-04394851 , version 1 (15-01-2024)

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Rachid Guerraoui, Anne-Marie Kermarrec, Guilhem Niot, Olivier Ruas, François Taïani. GoldFinger: Fast & Approximate Jaccard for Efficient KNN Graph Constructions. IEEE Transactions on Knowledge and Data Engineering, 2023, 35 (11), pp.11461-11475. ⟨10.1109/TKDE.2022.3232689⟩. ⟨hal-04394851⟩
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