Anonymize Croissant metadata for double-blind review
Browse files- etg_croissant.json +6 -6
etg_croissant.json
CHANGED
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@@ -23,13 +23,13 @@
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"zh",
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"ru"
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],
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"citation": "
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"creator": [
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{
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"@type": "sc:Person",
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"name": "
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"email": "
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"affiliation": "
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}
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],
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"keywords": [
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@@ -91,7 +91,7 @@
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"prov:wasGeneratedBy": {
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"@type": "prov:Activity",
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"name": "HackerSignal ETL Pipeline",
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"description": "Collection scripts executed
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"prov:startedAtTime": "2026-01-01",
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"prov:endedAtTime": "2026-04-30"
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},
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@@ -110,7 +110,7 @@
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"rai:personalSensitiveInformation": "No private communications, medical records, financial records, or government classified information is intentionally included. All text was collected from publicly accessible sources. Author strings are pseudonymized via SHA-256 hashing of source-namespaced author identifiers; original author strings are not recoverable from the released dataset. Public forum usernames are not distributed. The release supports takedown and correction requests through the repository issue tracker.",
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"rai:dataUseCases": "Intended uses: defensive cybersecurity research, temporal retrieval, provenance-aware benchmark design, vulnerability lifecycle analysis, exploit prediction and early warning systems, cross-lingual threat intelligence, graph neural network research on exploit\u2013CVE\u2013fix commit knowledge graphs, and pre-training domain-adapted language models for cybersecurity. Prohibited uses: training systems for automated exploit code generation or malware; de-anonymizing forum participants or linking pseudonymous identities outside approved research protocols; operational blocking, law-enforcement, or employment decisions based solely on model outputs; republishing source text from metadata_or_pointer_only sources.",
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"rai:dataSocialImpact": "HackerSignal enables earlier detection of vulnerability exploitation activity by connecting hacker community discourse to the CVE lifecycle. Positive impacts include faster defensive response times and better understanding of exploit emergence patterns. Potential negative impacts include misuse for automated attack generation or targeting of individuals; these are mitigated through responsible-use restrictions, author pseudonymization, source-level release governance, and explicit prohibition of offensive applications in the dataset license.",
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"rai:dataReleaseMaintenancePlan": "Maintained by
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"rai:hasSyntheticData": "none",
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"distribution": [
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{
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"zh",
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"ru"
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],
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"citation": "Anonymous. (2026). HackerSignal \u2014 A Multi-Source Dataset for Hacker Community Analysis and CVE Linkage. Proceedings of NeurIPS Evaluations & Datasets Track.",
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"creator": [
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{
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"@type": "sc:Person",
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"name": "Anonymous",
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"email": "anonymous@example.com",
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"affiliation": "Anonymous"
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}
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],
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"keywords": [
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"prov:wasGeneratedBy": {
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"@type": "prov:Activity",
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"name": "HackerSignal ETL Pipeline",
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"description": "Collection scripts executed between January and April 2026 using public APIs, public dataset ingestion, web scraping of publicly accessible pages, and pre-processed dataset imports. The pipeline performs HTML stripping, encoding normalization, text truncation (8000 chars), exact deduplication by SHA-256 fingerprint, author pseudonymization, and source-layer normalization.",
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"prov:startedAtTime": "2026-01-01",
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"prov:endedAtTime": "2026-04-30"
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},
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| 110 |
"rai:personalSensitiveInformation": "No private communications, medical records, financial records, or government classified information is intentionally included. All text was collected from publicly accessible sources. Author strings are pseudonymized via SHA-256 hashing of source-namespaced author identifiers; original author strings are not recoverable from the released dataset. Public forum usernames are not distributed. The release supports takedown and correction requests through the repository issue tracker.",
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| 111 |
"rai:dataUseCases": "Intended uses: defensive cybersecurity research, temporal retrieval, provenance-aware benchmark design, vulnerability lifecycle analysis, exploit prediction and early warning systems, cross-lingual threat intelligence, graph neural network research on exploit\u2013CVE\u2013fix commit knowledge graphs, and pre-training domain-adapted language models for cybersecurity. Prohibited uses: training systems for automated exploit code generation or malware; de-anonymizing forum participants or linking pseudonymous identities outside approved research protocols; operational blocking, law-enforcement, or employment decisions based solely on model outputs; republishing source text from metadata_or_pointer_only sources.",
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"rai:dataSocialImpact": "HackerSignal enables earlier detection of vulnerability exploitation activity by connecting hacker community discourse to the CVE lifecycle. Positive impacts include faster defensive response times and better understanding of exploit emergence patterns. Potential negative impacts include misuse for automated attack generation or targeting of individuals; these are mitigated through responsible-use restrictions, author pseudonymization, source-level release governance, and explicit prohibition of offensive applications in the dataset license.",
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"rai:dataReleaseMaintenancePlan": "Maintained by the dataset authors. Annual updates planned to incorporate new CVE records, CISA KEV entries, and newly published advisories. Versioning follows semantic versioning (MAJOR.MINOR.PATCH). All tagged versions remain available on HuggingFace Hub. Takedown and correction requests acknowledged within 10 business days; disputed text removed in next patch release. Sources that change terms post-release are downgraded to metadata_or_pointer_only until permissions are clarified.",
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"rai:hasSyntheticData": "none",
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"distribution": [
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{
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