A Provenance Model for the European Union General Data Protection Regulation
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In practice, decision provenance may require a combination of capture mechanisms to ensure that the appropriate information is recorded.
“In practice, it is likely that decision provenance may require a combination of capture mechanisms to ensure that the appropriate information is recorded [45], [46], 22”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [46]
Cites
What the GDPR means for businesses
Tankard, C. (2016) · Network Security · doi:10.1016/s1353-4858(16)30056-3 · Cited by 236 · see citing works
Trustworthy whole-system provenance for the Linux kernel
Bates, A., Tian, D. J., Butler, K. R. B., et al. (2015) · USENIX Security Symposium · Cited by 165 · see citing works
Self-citationCamflow: Managed Data-Sharing for Cloud Services
Pasquier, T., Singh, J. P., Eyers, D. M., et al. (2015) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2489211 · Cited by 88 · see citing works
On Purpose and by Necessity: Compliance Under the GDPR
Basin, D., Debois, S., & Hildebrandt, T. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-58387-6_2 · Cited by 73 · see citing works
Transparent Web Service Auditing via Network Provenance Functions
Bates, A., Hassan, W. U., Butler, K. R. B., et al. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052640 · Cited by 40 · see citing works
Self-citationModelling Provenance for GDPR Compliance using Linked Open Data Vocabularies
Pandit, H. J., & Lewis, D. D. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3246474 · Cited by 39 · see citing works
Provenance-based Auditing of Private Data Use
Aldeco-Pérez, R., & Moreau, L. (2008) · Electronic workshops in computing · doi:10.14236/ewic/vocs2008.13 · Cited by 33 · see citing works
Gjermundrød, H., Dionysiou, I., & Costa, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46963-8_1 · Cited by 33 · see citing works
Using Ontologies to Model Data Protection Requirements in Workflows
Bartolini, C., Muthuri, R., & Santos, C. T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-50953-2_17 · Cited by 32 · see citing works
Transparent Personal Data Processing: The Road Ahead
Bonatti, P. A., Kirrane, S., Polleres, A., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66284-8_28 · Cited by 32 · see citing works
How Usage Control and Provenance Tracking Get Together - A Data Protection Perspective
Bier, C. (2013) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2013.24 · Cited by 20 · see citing works
Provenance as a security control
Martin, A., Lyle, J., & Namilkuo, C. (2012) · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Decision Provenance: Harnessing Data Flow for Accountable Systems
Singh, J., Cobbe, J., & Norval, C. (2019) · IEEE Access · doi:10.1109/access.2018.2887201 · Cited by 110 · see citing works
- Authors
- Ujcich, B. E., Bates, A., & Sanders, W. H.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Ujcich, Benjamin E.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 45-57
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2890992458
- Cited by
- 13
- References
- 12
- Citations by year
- 2023: 2 · 2022: 1 · 2021: 5 · 2020: 1 · 2019: 2 · 2018: 2
- FWCI
- 23.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- General Data Protection Regulation, GDPR compliance, data provenance, personal data, data privacy regulation, provenance model, compliance verification, data processing workflow, design patterns
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
