On Optimizing the Trade-off between Privacy and Utility in Data Provenance
Summary: Formalizes privacy-utility trade-off in data provenance via provenance abstraction; privacy = queries matching obfuscated provenance (k-anonymity style), utility = entropy of the abstraction. Shows intractability; proposes greedy heuristics exploiting provenance structure and validates on TPC-H/IMDB. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Daniel Deutch
- 2. Ariel Frankenthal
- 3. Amir Gilad
- 4. Yuval Moskovitch
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,768 | DPXPlain: Privately Explaining Aggregate Query Answers | 2023 | VLDB | 4.2815042e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 31 | Provenance Semirings | 2007 | PODS | 0.00078516827 |
| 487 | Why Not? | 2009 | SIGMOD | 0.00022030123 |
| 1,106 | Provenance for Aggregate Queries | 2011 | PODS | 0.00013976386 |
| 1,502 | Discovering Queries based on Example Tuples | 2014 | SIGMOD | 0.00011614522 |
| 1,575 | Reverse Engineering Complex Join Queries | 2013 | SIGMOD | 0.00011288804 |
| 2,990 | FastQRE: Fast Query Reverse Engineering | 2018 | SIGMOD | 7.7727915e-05 |
| 3,158 | Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems | 2019 | VLDB | 7.466958e-05 |
| 3,668 | Reverse Engineering Aggregation Queries | 2017 | VLDB | 6.8581987e-05 |
| 4,708 | Aggregation in Probabilistic Databases via Knowledge Compilation | 2012 | VLDB | 5.9763416e-05 |
| 6,511 | Provenance Views for Module Privacy | 2011 | PODS | 5.0273291e-05 |
| 7,131 | Enabling Privacy in Provenance-Aware Workflow Systems | 2011 | CIDR | 4.8181343e-05 |
| 8,392 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.5234647e-05 |
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|---|---|---|---|---|
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| 1,639 | Injecting Utility into Anonymized Datasets | 2006 | SIGMOD | 0.00011049413 |
| 9,058 | Tracking Personal Data Use: Provenance And Trust | 2015 | CIDR | 4.3997447e-05 |
| 2,182 | Querying Data Provenance | 2010 | SIGMOD | 9.3596252e-05 |
| 7,416 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 4.7309698e-05 |
| 653 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB | 0.00018616975 |
| 7,131 | Enabling Privacy in Provenance-Aware Workflow Systems | 2011 | CIDR | 4.8181343e-05 |
| 1,760 | The Boundary Between Privacy and Utility in Data Publishing | 2007 | VLDB | 0.00010641674 |
| 8,392 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.5234647e-05 |
| 6,187 | On Provenance Minimization | 2011 | PODS | 5.1611195e-05 |