Quotient Semivalues for False-Name-Resistant Data Attribution

• URL: http://arxiv.org/abs/2605.07663v1
• Date: Fri, 08 May 2026 12:34:20 GMT
• Title: Quotient Semivalues for False-Name-Resistant Data Attribution
• Abstract summary: We formalize false-name manipulation in ML data attribution.<n>We compute Shapley-, Banzhaf-, or Beta-style values over evidence-backed attribution clusters.<n>We instantiate the mechanisms in DataMarket-Gym, a benchmark for attribution under strategic provider attacks.
• Score: 1.253312107729806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data valuation methods allocate payments and audit training data's contribution to machine-learning pipelines; however, they often assume passive contributors. In reality, contributors can split datasets across pseudonymous identities, duplicate high-value examples, create near-duplicates, or launder synthetic variants to inflate their share. We formalize this as false-name manipulation in ML data attribution. Our main construction is the quotient semivalue mechanism: compute Shapley-, Banzhaf-, or Beta-style values over evidence-backed attribution clusters instead of raw identities, using a canonical-representative operator to absorb within-cluster duplication. We prove an impossibility: on a fixed monotone data-value game, exact Shapley-fair attribution over reported identities is incompatible with unrestricted false-name-proofness, even on binary-valued instances, and characterize the split-gain of a general semivalue on a unanimity counter-example. The mechanism is exactly false-name-proof under two structural conditions: false-name-neutral within-cluster allocation and quotient-stable manipulations. Under imperfect provenance, when these conditions hold approximately, manipulation gain and fairness loss are bounded by three measurable quantities: escaped-cluster mass, value-estimation error, and clustering distance. We instantiate the mechanisms in DataMarket-Gym, a benchmark for attribution under strategic provider attacks. On synthetic classification tasks, quotient semivalues with example-level evidence reduce manipulation gain on duplicate and near-duplicate Sybil attacks from $1.74$ under baseline Shapley to $0.96$, near the honest level. The cosine-threshold and (false-merge, false-split) rate sweeps trace the corresponding fairness--Sybil frontier.

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