Reproducibility Report for ACM SIGMOD 2024 Paper: 'DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance'
The paper [1] introduces DProvDB, a fine-grained privacy provenance framework. Our reproducibility evaluation shows that the key findings of the paper have been successfully reproduced, with minor discrepancies from the original figures explained by inherent randomness.
Authors
- Shufan Zhang (ORCID: https://orcid.org/0000-0002-0983-2730)
- Giorgio Vinciguerra (ORCID: https://orcid.org/0000-0003-0328-7791)
- Chuqing Gao (ORCID: https://orcid.org/0009-0008-2171-5036)
- Xi He
Institutions
- University of Pisa (IT)
- University of Waterloo (CA)
- Purdue University West Lafayette (US)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1145/3687998.3717043
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00