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Revisiting Elliptic++ for Bitcoin Forensics: Feature Reconstruction and Leakage-Resistant Evaluation — arXiv:2611.04000

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04000
License (as archived): CC0-1.0
Archived: 2026-11-04T12:38:44.398Z
SHA-256 of the archived text: fad544dd711e4551585ce4dd2b1b9f477979517b19131c84b056bf067b780f04
Stable URL of this page: https://metraipolis.com/documento/dc_656458a501ca

How to cite

Revisiting Elliptic++ for Bitcoin Forensics: Feature Reconstruction and Leakage-Resistant Evaluation — arXiv:2611.04000 — arXiv (en), https://arxiv.org/abs/2611.04000 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_656458a501ca (Metraipolis archive dc_656458a501ca, sha256 fad544dd711e4551585ce4dd2b1b9f477979517b19131c84b056bf067b780f04)

BibTeX

@misc{dc656458a501ca,
  title = {Revisiting Elliptic++ for Bitcoin Forensics: Feature Reconstruction and Leakage-Resistant Evaluation — arXiv:2611.04000},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04000},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_656458a501ca; sha256 fad544dd711e4551585ce4dd2b1b9f477979517b19131c84b056bf067b780f04},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04000. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Elliptic and its address-level extension Elliptic++ are widely used benchmark datasets in Bitcoin forensics, cited by more than 1300 academic studies according to Google Scholar. We identify future information leakage in Elliptic++. Its positional data split does not respect temporal order, and the exact definitions of its address-level features remain undocumented. We revisit how the 55 Elliptic++ address-level features are computed and propose a leakage-resistant pipeline. First, the exact feature definitions are recovered by linking Elliptic transactions to public ledger data. Second, we compute the features with a temporal split based on each address's first appearance, using only information available at the time of prediction. Once temporal ordering is enforced, the random forest (RF)'s strong predictive performance largely disappears: its F1 score on the illicit class falls from $0.846$ under the original leaky evaluation to just $0.291$ under our leakage-resistant pipeline. Furthermore, RF no longer outperforms a simple logistic regression baseline. We provide the exact feature definitions and executable code to reproduce the leakage-resistant features, so that future Bitcoin forensic models can be evaluated based on genuine predictive capability rather than temporal data leakage.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2611.04000 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:29:17.129Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).