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Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
Summary: RiseFL achieves simultaneous input privacy and integrity in federated learning with low-cost ZKPs by converting strict integrity checks into probabilistic hypothesis tests and using a hybrid commitment. Optimized ZKP gen/verify yields 28–164× client speedups vs ACORN/RoFL/EIFFeL with formal proofs and experiments.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13462
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.5057547e-05
- Overall Rank
- 8,332 | 42.10%
- DOI
-
10.14778/3665844.3665860
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 1,207 |
Privacy Preserving Vertical Federated Learning for Tree-based Models |
2020 |
VLDB |
0.00011770793 |
| 1,929 |
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning |
2021 |
SIGMOD |
9.5547439e-05 |
| 3,185 |
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data |
2022 |
SIGMOD |
7.7267866e-05 |
| 4,410 |
Projected Federated Averaging with Heterogeneous Differential Privacy |
2022 |
VLDB |
6.7812717e-05 |
| 5,052 |
Enabling SQL-based Training Data Debugging for Federated Learning |
2022 |
VLDB |
6.4521864e-05 |
| 5,922 |
ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs |
2023 |
VLDB |
6.1043252e-05 |
| 6,285 |
Practical Differentially Private and Byzantine-resilient Federated Learning |
2023 |
SIGMOD |
5.989696e-05 |
| 6,326 |
Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System |
2023 |
VLDB |
5.9730345e-05 |
| 6,344 |
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy |
2022 |
VLDB |
5.9694281e-05 |
| 7,559 |
ExDRa: Exploratory Data Science on Federated Raw Data |
2021 |
SIGMOD |
5.6533871e-05 |
| 7,794 |
Incentive-Aware Decentralized Data Collaboration |
2023 |
SIGMOD |
5.6035684e-05 |
| 8,696 |
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity |
2023 |
VLDB |
5.4478124e-05 |
| 9,333 |
FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data |
2023 |
SIGMOD |
5.3449422e-05 |
| 11,189 |
Regularized Pairwise Relationship based Analytics for Structured Data |
2023 |
SIGMOD |
5.1725247e-05 |
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| Overall Rank |
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| 5,519 |
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2022 |
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| 1,207 |
Privacy Preserving Vertical Federated Learning for Tree-based Models |
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| 11,240 |
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| 4,228 |
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2023 |
VLDB |
6.8829966e-05 |
| 4,410 |
Projected Federated Averaging with Heterogeneous Differential Privacy |
2022 |
VLDB |
6.7812717e-05 |
| 11,046 |
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2024 |
VLDB |
5.1725247e-05 |
| 6,285 |
Practical Differentially Private and Byzantine-resilient Federated Learning |
2023 |
SIGMOD |
5.989696e-05 |
| 8,548 |
Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation |
2024 |
VLDB |
5.4742553e-05 |
| 3,185 |
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data |
2022 |
SIGMOD |
7.7267866e-05 |