Peter‐Paul de Wolf
Impact in
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Internet Traffic Analysis and Secure E-voting
- Statistics and Probability top 10%
- Statistical Methods and Bayesian Inference
Papers in
-
- Privacy-Preserving Technologies in Data 2
- Data Analysis with R 1
- Bayesian Modeling and Causal Inference 1
-
- Data Quality and Management 3
- Co-authors
- Sarah Giessing (2 shared papers)Luisa Franconi (1 shared paper)Keith Spicer (1 shared paper)Anco Hundepool (1 shared paper)Eric Schulte Nordholt (1 shared paper)Leon Willenborg (1 shared paper)P.G.M. van der Heijden (1 shared paper)Bart Bakker (1 shared paper)
- Journals
- Data & Knowledge Engineering (1 paper)Journal of Official Statistics (2 papers)RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya) (1 paper)Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) (1 paper)CERN Document Server (European Organization for Nuclear Research) (1 paper)
- Partner nations
- NetherlandsGermany
In The Last Decade
Peter‐Paul de Wolf
6 papers receiving 210 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 160
- Statistics and Probability 31
- Computer Science Applications 20
- Management Science and Operations Research 40
- Sociology and Political Science 65
Countries citing papers authored by Peter‐Paul de Wolf
This map shows the geographic impact of Peter‐Paul de Wolf's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter‐Paul de Wolf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter‐Paul de Wolf more than expected).
Fields of papers citing papers by Peter‐Paul de Wolf
This network shows the impact of papers produced by Peter‐Paul de Wolf. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter‐Paul de Wolf. The network helps show where Peter‐Paul de Wolf may publish in the future.
Co-authors
The 8 scholars most cited alongside Peter‐Paul de Wolf, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 210 | |
| 2 | 2019 | 3 | |
| 3 | The post randomisation method for protecting microdata | 1998 | 2 |
| 4 | Distribution, patchiness, and population densities of Pleurobrachia pileus explained | 2012 | 2 |
| 5 | Challenges for statistical disclosure control in a world with big data and open data | 2015 | 1 |
| 6 | 2021 | 1 | |
| 7 | 2009 | 1 |
About Peter‐Paul de Wolf
Peter‐Paul de Wolf is a scholar working on Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Epidemiology and Information Systems, having authored 7 papers that have together received 220 indexed citations. Recurring topics across this work include Data Quality and Management (3 papers), Privacy-Preserving Technologies in Data (2 papers), Census and Population Estimation (2 papers), Data-Driven Disease Surveillance (2 papers), Data Analysis with R (1 paper), Water Quality and Resources Studies (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Survey Sampling and Estimation Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (160 citations), Statistics and Probability (31 citations), Computer Science Applications (20 citations), Management Science and Operations Research (40 citations) and Sociology and Political Science (65 citations). Peter‐Paul de Wolf has collaborated with scholars based in Netherlands and Germany. Frequent co-authors include Sarah Giessing, Luisa Franconi, Keith Spicer, Anco Hundepool, Eric Schulte Nordholt, Leon Willenborg, P.G.M. van der Heijden and Bart Bakker. Their work appears in journals such as Data & Knowledge Engineering, Journal of Official Statistics, RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya), Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) and CERN Document Server (European Organization for Nuclear Research).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.