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: 13649
Venue: VLDB
Year: 2024
Pagerank: 5.4217837e-05
Overall Rank: 8,456 | 41.99%
DOI: 10.14778/3665844.3665860

Incoming Non-self Citations Over Time

Authors

1. Yizheng Zhu (National University of Singapore)
2. Yuncheng Wu (National University of Singapore; Renmin University of China)
3. Zhaojing Luo (Beijing Institute of Technology; National University of Singapore)
4. Beng Chin Ooi (National University of Singapore)
5. Xiaokui Xiao (National University of Singapore)

BibTeX Citation

@article{zhu_vldb24,
        title = {{Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs}},
        author = {Zhu, Yizheng and Wu, Yuncheng and Luo, Zhaojing and Ooi, Beng Chin and Xiao, Xiaokui},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {9},
        pages = {2321--2334},
        doi = {10.14778/3665844.3665860},
        url = {https://doi.org/10.14778/3665844.3665860},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,770	PoneglyphDB: Efficient Non-interactive Zero-Knowledge Proofs for Arbitrary SQL-Query Verification	2025	SIGMOD	5.2209769e-05
10,391	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	5.093636e-05

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
1,249	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011495357
1,959	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.4090198e-05
3,237	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD	7.6089416e-05
4,446	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB	6.6990707e-05
5,131	Enabling SQL-based Training Data Debugging for Federated Learning	2022	VLDB	6.3537809e-05
6,010	ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs	2023	VLDB	6.0112251e-05
6,366	Practical Differentially Private and Byzantine-resilient Federated Learning	2023	SIGMOD	5.8983442e-05
6,406	Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy	2022	VLDB	5.885424e-05
6,421	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.8819368e-05
7,687	ExDRa: Exploratory Data Science on Federated Raw Data	2021	SIGMOD	5.5671645e-05
7,911	Incentive-Aware Decentralized Data Collaboration	2023	SIGMOD	5.5181056e-05
8,814	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	2023	VLDB	5.3647251e-05
9,473	FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data	2023	SIGMOD	5.2634238e-05
11,391	Regularized Pairwise Relationship based Analytics for Structured Data	2023	SIGMOD	5.093636e-05

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