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DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration

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arXiv:2606. 21240v1 Announce Type: new Abstract: Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying.

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Authors: Tian Dong, Yan Meng, Shaofeng Li, Guoxing Chen, Yuling Chen, Zhen Liu, Haojin Zhu, Hao Chen
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, rate, full, test, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.21240

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