The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Big Data and Analytics in the Age of the GDPR
Abstract
The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks. We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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 the citing paper
“For narrowly tailored AI models based on supervised learning strategies, one may argue that AI training is not particularly harmful as it does not, generally, reveal any new information about the data subjects themselves [29 -31].”
From Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity (2024) · cites this paper as [31]
Citations
Cites
The Description Logic Handbook: Theory, Implementation and Applications
(2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511711787 · Cited by 6,147
The Description Logic Handbook – Theory, Implementation and Applications
Mann, C. J. H. (2003) · Kybernetes · doi:10.1108/k.2003.06732iae.006 · Cited by 2,224
Flexible support for multiple access control policies
Jajodia, S., Samarati, P., Sapino, M. L., & Subrahmanian, V. S. (2001) · ACM Transactions on Database Systems · doi:10.1145/383891.383894 · Cited by 640
Glimm, B., Horrocks, I., Motik, B., Stoilos, G., & Wang, Z. (2014) · Journal of Automated Reasoning · doi:10.1007/s10817-014-9305-1 · Cited by 620
Kifer, D., & Machanavajjhala, A. (2011) · Proceedings of the 2011 ACM SIGMOD International Conference on Management of data · doi:10.1145/1989323.1989345 · Cited by 619
A policy language for a pervasive computing environment
Kagal, L., Finin, T., & Joshi, A. (2004) · Proceedings POLICY 2003. IEEE 4th International Workshop on Policies for Distributed Systems and Networks · doi:10.1109/policy.2003.1206958 · Cited by 516
Automatic generation of natural language summaries for Java classes
Moreno, L., Aponte, J., Sridhara, G., Marcus, A., Pollock, L., & Vijay-Shanker, K. (2013) · 2013 21st International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2013.6613830 · Cited by 331
Carmona, J., van Dongen, B. F., Solti, A., & Weidlich, M. (2018) · Springer eBooks · doi:10.1007/978-3-319-99414-7 · Cited by 330
Uszok, A., Bradshaw, J., Jeffers, R., Suri, N., Hayes, P., Breedy, M., Bunch, L., Johnson, M., Kulkarni, S., & Lott, J. (2004) · Proceedings POLICY 2003. IEEE 4th International Workshop on Policies for Distributed Systems and Networks · doi:10.1109/policy.2003.1206963 · Cited by 328
The right to data portability in the GDPR: Towards user-centric interoperability of digital services
De Hert, P., Papakonstantinou, V., Malgieri, G., Beslay, L., & Sanchez, I. (2018) · Computer Law & Security Review · doi:10.1016/j.clsr.2017.10.003 · Cited by 262
Research works printed but not linked: 11
- engineering, 2016, Kafka inside keynote pipeline
- engineering, 2015, Running Kafka At Scale
- svingen, 2017, Publishing with apache kafka at the new york times
- schlehan, 2018, Deliverable 1 6 - Legal requirements for a privacy enhancing Big Data V2
- dullaert, 2018, Deliverable 3 4 - Transparency & Compliance Release
- platzer, 2018, Enabling privacy-preserving big data
- 2017, Information Commissioner's Office (ICO) UK
- 2017, Microsoft Trust Center
- 0, GDPR Compliance Toolkit
- kasiviswanathan, 2008, A Note on Differential Privacy Defining Resistance to Arbitrary Side Information, abs 803 3946
- kirrane, 2018, Deliverable 2 7 - Transparency Framework V2
Cited by
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466
“While scholars are divided about whether the use of personal data for machine learning purposes should be permitted under the balancing test (permissive view: [26]; more restrictive view: [109, 117]; see also [118]), the threat of model inversion arguably weighs in favor of data subjects.”
BackgroundGenerative AI in EU law: Liability, privacy, intellectual property, and cybersecurity
Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024) · Computer Law & Security Review · doi:10.1016/j.clsr.2024.106066 · Cited by 124
Generative Ai in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity
Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024) · doi:10.2139/ssrn.4821952 · Cited by 57
Metadata
- Authors
- Bonatti, P. A., & Kirrane, S.
- Affiliations
- Università di Napoli Federico II
- Vienna University of Economics and Business
- Venue
- 2019 IEEE International Congress on Big Data (BigDataCongress)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 7-16
- Cited by
- 16
- References
- 36
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Big Data and Analytics in the Age of the GDPR. https://app.cochat.ai/papers/10.1109/bigdatacongress.2019.00015
- The paper
- Bonatti, P. A., & Kirrane, S. (2019). Big Data and Analytics in the Age of the GDPR. 2019 IEEE International Congress on Big Data (BigDataCongress). https://doi.org/10.1109/bigdatacongress.2019.00015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.