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PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning

Summary: PS‑MI: model‑agnostic VFDV that values coalitions by mutual information (features→labels), estimated via K‑NN with stratified sampling to avoid enumerating all coalitions. Privacy via random projection (DP, unbiased distances) instead of HE; LSH, batching, and early stop speed up private K‑NN—higher accuracy and up to 592× faster. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13982
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,686 | 25.66%
DOI
10.14778/3748191.3748215

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

Rank Cited Paper Year Venue Pagerank
72 Combining Fuzzy Information from Multiple Systems 1996 PODS 0.00058577335
1,143 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00013710269
1,298 Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms 2019 VLDB 0.00012758104
1,895 VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning 2021 SIGMOD 0.00010180896
3,506 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0291192e-05
4,377 Understanding and Benchmarking the Impact of GDPR on Database Systems 2020 VLDB 6.2404627e-05
4,753 Secure Shapley Value for Cross-Silo Federated Learning 2023 VLDB 5.9469115e-05
5,507 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4724291e-05
5,775 Federated Matrix Factorization with Privacy Guarantee 2022 VLDB 5.3310992e-05
6,459 Practical Differentially Private and Byzantine-resilient Federated Learning 2023 SIGMOD 5.0556005e-05
6,502 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.0361846e-05
6,700 Differentially Private Vertical Federated Clustering 2023 VLDB 4.9563668e-05
7,380 Efficient Sampling Approaches to Shapley Value Approximation 2023 SIGMOD 4.746272e-05
8,666 Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation 2024 VLDB 4.471975e-05
9,323 FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data 2023 SIGMOD 4.3556432e-05
9,966 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2269436e-05
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