Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs
Authors: Sara Ahmadian, Edith Cohen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our attack used only 4k queries with the widely used Hyper Log Log (HLL++) (Flajolet et al., 2007b; Heule et al., 2013) sketch. We conduct an empirical evaluation of our proposed attack on the Hyper Log Log (HLL) sketch (Durand & Flajolet, 2003; Flajolet et al., 2007a) with the HLL++ estimator (Heule et al., 2013). |
| Researcher Affiliation | Collaboration | 1Google Research, United States 2Department of Computer Science, Tel Aviv University, Israel. |
| Pseudocode | Yes | Algorithm 1: Attack standard estimators. Algorithm 3: Single Batch Attacker. Algorithm 4: Adaptive Attacker. |
| Open Source Code | No | The paper mentions utilizing 'the open-source implementation of HLL++ algorithm in github' but does not state that the code developed for this paper is open-source or provide a link to it. |
| Open Datasets | No | The paper states: 'To generate the data... we generate random strings using the English alphabet of a fixed length'. It does not refer to a publicly available dataset or provide access information for the data generated for the experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or detailed methodology for splitting into training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions utilizing 'the open-source implementation of HLL++ algorithm in github' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the size of our ground set n k to be in this relevant regime. We consider two different error rates, ฯต = 0.1 with corresponding sketch size k = 104 and ฯต = 0.05, with corresponding sketch size k = 416. We use the same ground set comprising of 5000 keys for both sets of experiments. For each sketch size k, we generate a ground set of size n = 10 ยท 10 log10(k) to ensure that the ground set is larger than sketch size and the Min Hash component of the HLL++ estimator is used. |