Privacy-preserving and Verifiable Causal Prescriptive Analytics

Summary: zkCLEAR: a ZKP-based causal inference framework proving prescriptive recommendations without revealing data or models, enabling verifiable, privacy-preserving prescriptive analytics. Designs ZKP-friendly causal operators and workflow decomposition, yielding major efficiency gains (up to 35.1× faster proofs, 214.5× smaller proofs) over general-purpose ZKP systems. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID: 7601
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,391 | 28.71%
DOI: 10.1145/3769815

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Zhaoyu Wang (Hong Kong University of Science and Technology)
2. Pingchuan Ma (Hong Kong University of Science and Technology)
3. Zhantong Xue (Hong Kong University of Science and Technology)
4. Yanbo Dai (Hong Kong University of Science and Technology)
5. Zhenlan Ji (Hong Kong University of Science and Technology)
6. Shuai Wang (Hong Kong University of Science and Technology)

BibTeX Citation

@inproceedings{wang_sigmod26,
        title = {{Privacy-preserving and Verifiable Causal Prescriptive Analytics}},
        author = {Wang, Zhaoyu and Ma, Pingchuan and Xue, Zhantong and Dai, Yanbo and Ji, Zhenlan and Wang, Shuai},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769815},
        url = {https://dl.acm.org/doi/10.1145/3769815},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
605	The Complexity of Causality and Responsibility for Query Answers and non-Answers	2011	VLDB	0.00015839628
663	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00015174751
863	Interventional Fairness : Causal Database Repair for Algorithmic Fairness	2019	SIGMOD	0.00013531835
1,249	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011495357
2,374	Causal Relational Learning	2020	SIGMOD	8.6755064e-05
3,000	Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals	2021	SIGMOD	7.8677069e-05
4,876	XInsight: eXplainable Data Analysis Through The Lens of Causality	2023	SIGMOD	6.4687705e-05
5,025	HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach	2022	SIGMOD	6.3974766e-05
5,286	Data is Dead… Without What-If Models	2011	VLDB	6.282151e-05
6,010	ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs	2023	VLDB	6.0112251e-05
6,054	Causal Data Integration	2023	VLDB	5.9932261e-05
6,352	OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport	2024	SIGMOD	5.9042757e-05
6,932	Summarized Causal Explanations For Aggregate Views	2024	SIGMOD	5.7362362e-05
8,158	Continual Observation of Joins under Differential Privacy	2024	SIGMOD	5.4756587e-05
8,456	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	5.4217837e-05
9,770	PoneglyphDB: Efficient Non-interactive Zero-Knowledge Proofs for Arbitrary SQL-Query Verification	2025	SIGMOD	5.2209769e-05
9,773	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	5.2209769e-05
9,785	Secure Sampling for Approximate Multi-party Query Processing	2023	SIGMOD	5.2209769e-05
9,786	TransNet: Training Privacy-Preserving Neural Network over Transformed Layer	2020	VLDB	5.2209769e-05

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