Duplicate detection is the problem of identifying whether a given item has previously appeared in a (possibly infinite) stream of data, when only a limited amount of memory is available. Unfortunately the infinite stream setting is ill-posed, and error rates of duplicate detection filters turn out to be heavily constrained: consequently they appear to provide no advantage, asymptotically, over a biased coin toss[8]. In this paper we formalize the sliding window setting introduced by[12, 15], and show that a perfect (zero error) solution can be used up to a maximal window size. Above this threshold we show that some existing duplicate detection filters (designed for the non-windowed setting) perform better that those targeting the windowed problem. Finally, we introduce a “queuing construction” that improves on the performance of some duplicate detection filters in the windowed setting. We also analyse the security of our filters in an adversarial setting.
CITATION STYLE
Géraud-Stewart, R., Lombard-Platet, M., & Naccache, D. (2020). Approaching Optimal Duplicate Detection in a Sliding Window. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12273 LNCS, pp. 64–84). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58150-3_6
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