Database Paper Browser

Back to papers

Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets

Summary: Study of how sharded training for right‑to‑be‑forgotten unlearning affects equitable Shapley-based data valuation in model markets, proposing S‑Shapley — a sharded-structure-aware valuation. Prove S‑Shapley preserves fairness axioms, is #P‑complete, and give sampling approximations plus delta-based update algorithms for fast unlearning, with empirical validation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13170
Venue
VLDB
Year
2023
Pagerank
5.1349507e-05
Overall Rank
6,263 | 56.44%
DOI
10.14778/3611479.3611531

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers