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| privacy:javascript [2026/09/03 22:13] โ Generic-review fixes: repoint the cross-page anchor after the target heading was renamed, replace 'top 7%' with the corrected 20.3% resolvable-model figure, and correct 'the two lowest non-empty rows' (javascript and fingerprinting-script are zero rows, t karel.kubicek.claude | privacy:javascript [2026/09/03 22:34] (current) โ Re-review fix: the resolvable-model figure moved to 19.4% of 175 after the buckets were restricted to papers that used an LLM rather than compared against one. Authored by Claude karel.kubicek.claude |
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| **The nearest peer-reviewed work is one layer down, at the request.** TGNN {[xiong2026_tgnn]} (TheWebConf 2026) uses Qwen3 to label HTTP request/response quadruples as tracking or not, and reports an annotation F1 of **98.17%** against expert labels where filter lists reach **55.14%** on the same ground truth.((The paper reports this figure twice as **98.17%** โ in ยง4.1.3 //LLM-based Labeling// ("๐๐ performs well (๐น1-score of 98.17%)") and again beside Figure 5 โ and once as **98.19%**, in the contributions paragraph of its Introduction. Its abstract states no F1 for the annotation component at all. The discrepancy is the paper's, not ours; the body figure is quoted here and on [[privacy:requests]], so the two pages agree. The 55.14% filter-list comparison appears only in the Introduction. Located in ''paper.cols.txt'' on 2026-09-03; note that this file contains NUL bytes, so shell ''grep'' reports nothing without ''-a''.)) It is not a script classifier โ it explicitly contrasts its approach with prior methods that do "single-domain analysis-such as string matching against domain lists or scrutinizing JavaScript execution within a page" โ but it is the closest thing to a citable precedent for using a model to //manufacture tracker labels//, which is this page's weakest link. [[privacy:requests]] treats it in full. Beyond it the adjacent work is not peer-reviewed and not on-point: LLM-aided **deobfuscation** feeding a graph classifier for JavaScript //malware//,((//Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection//, [[https://arxiv.org/abs/2507.22447|arXiv:2507.22447]], 2025.)) LLM screening of malicious npm packages, and ''humanify'', which uses a model only to //suggest identifier names// during de-minification.(([[https://github.com/jehna/humanify|github.com/jehna/humanify]], v3.1.1, checked 2026-08-06. The AST rewrite is done by ''oxc''; the model only proposes names.)) | **The nearest peer-reviewed work is one layer down, at the request.** TGNN {[xiong2026_tgnn]} (TheWebConf 2026) uses Qwen3 to label HTTP request/response quadruples as tracking or not, and reports an annotation F1 of **98.17%** against expert labels where filter lists reach **55.14%** on the same ground truth.((The paper reports this figure twice as **98.17%** โ in ยง4.1.3 //LLM-based Labeling// ("๐๐ performs well (๐น1-score of 98.17%)") and again beside Figure 5 โ and once as **98.19%**, in the contributions paragraph of its Introduction. Its abstract states no F1 for the annotation component at all. The discrepancy is the paper's, not ours; the body figure is quoted here and on [[privacy:requests]], so the two pages agree. The 55.14% filter-list comparison appears only in the Introduction. Located in ''paper.cols.txt'' on 2026-09-03; note that this file contains NUL bytes, so shell ''grep'' reports nothing without ''-a''.)) It is not a script classifier โ it explicitly contrasts its approach with prior methods that do "single-domain analysis-such as string matching against domain lists or scrutinizing JavaScript execution within a page" โ but it is the closest thing to a citable precedent for using a model to //manufacture tracker labels//, which is this page's weakest link. [[privacy:requests]] treats it in full. Beyond it the adjacent work is not peer-reviewed and not on-point: LLM-aided **deobfuscation** feeding a graph classifier for JavaScript //malware//,((//Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection//, [[https://arxiv.org/abs/2507.22447|arXiv:2507.22447]], 2025.)) LLM screening of malicious npm packages, and ''humanify'', which uses a model only to //suggest identifier names// during de-minification.(([[https://github.com/jehna/humanify|github.com/jehna/humanify]], v3.1.1, checked 2026-08-06. The AST rewrite is done by ''oxc''; the model only proposes names.)) |
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| <WRAP todo>Treat this as an opportunity, not a settled answer. If you are planning an LLM-based script classifier, you are not late โ but you also have no baseline to cite, so budget for building one, and for the reviewer question about cost, reproducibility and prompt/version drift that this page cannot yet answer for you. Two things you can borrow rather than invent: TGNN's annotation-versus-filter-list comparison {[xiong2026_tgnn]} is the experimental design a reviewer will expect, and the model-reporting figures on [[design:website_classification#And almost nobody names a model you could resolve]] show that only **20.3%** of the 177 corpus papers using an LLM name a model resolvable to an actual artefact โ so naming yours to the checkpoint is cheap novelty.</WRAP> | <WRAP todo>Treat this as an opportunity, not a settled answer. If you are planning an LLM-based script classifier, you are not late โ but you also have no baseline to cite, so budget for building one, and for the reviewer question about cost, reproducibility and prompt/version drift that this page cannot yet answer for you. Two things you can borrow rather than invent: TGNN's annotation-versus-filter-list comparison {[xiong2026_tgnn]} is the experimental design a reviewer will expect, and the model-reporting figures on [[design:website_classification#And almost nobody names a model you could resolve]] show that only **19.4%** of the 175 corpus papers that actually use an LLM name a model resolvable to an actual artefact โ so naming yours to the checkpoint is cheap novelty.</WRAP> |
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| ==== Two 2025 results that change how you design a crawl ==== | ==== Two 2025 results that change how you design a crawl ==== |
