Microsoft Purview: A System for Central Governance of Data
Summary: Microsoft Purview unifies automated discovery, sensitivity classification, and cross-estate ABAC over structured and unstructured data. Its Azure SQL–Office 365 integration enforces consistent policies across heterogeneous systems, with evaluated performance overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shafi Ahmad (Microsoft)
- 2. Dillidorai Arumugam (Microsoft)
- 3. Srdan Bozovic (Microsoft)
- 4. Elnata Degefa (Microsoft)
- 5. Sailesh Duvvuri (Microsoft)
- 6. Steven Gott (Microsoft)
- 7. Nitish Gupta (Microsoft)
- 8. Joachim Hammer (Microsoft)
- 9. Nivedita Kaluskar (Microsoft)
- 10. Raghav Kaushik (Microsoft)
- 11. Rakesh Khanduja (Microsoft)
- 12. Prasad Mujumdar (Microsoft)
- 13. Gaurav Malhotra (Microsoft)
- 14. Pankaj Naik (Microsoft)
- 15. Nikolas Ogg (Microsoft)
- 16. Krishna Kumar Parthasarthy (Microsoft)
- 17. Raghu Ramakrishnan (Microsoft)
- 18. Vlad Rodriguez (Microsoft)
- 19. Rahul Sharma (Microsoft)
- 20. Jakub Szymaszek (Microsoft)
- 21. Andreas Wolter (Microsoft)
BibTeX Citation
@article{ahmad_vldb23,
title = {{Microsoft Purview: A System for Central Governance of Data}},
author = {Ahmad, Shafi and Arumugam, Dillidorai and Bozovic, Srdan and Degefa, Elnata and Duvvuri, Sailesh and Gott, Steven and Gupta, Nitish and Hammer, Joachim and Kaluskar, Nivedita and Kaushik, Raghav and Khanduja, Rakesh and Mujumdar, Prasad and Malhotra, Gaurav and Naik, Pankaj and Ogg, Nikolas and Parthasarthy, Krishna Kumar and Ramakrishnan, Raghu and Rodriguez, Vlad and Sharma, Rahul and Szymaszek, Jakub and Wolter, Andreas},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3624--3635},
doi = {10.14778/3611540.3611552},
url = {https://doi.org/10.14778/3611540.3611552},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,701 | Unified Lineage System: Tracking Data Provenance at Scale | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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