Peter Christen, Ph.D.
Affiliations: | Computer Science | Australian National University, Acton, Australian Capital Territory, Australia |
Area:
data matching, data miningWebsite:
http://cs.anu.edu.au/~Peter.Christen/Google:
"Peter Christen"Mean distance: 106866
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Publications
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| Vidanage A, Ranbaduge T, Christen P, et al. (2020) A Privacy Attack on Multiple Dynamic Match-key based Privacy-Preserving Record Linkage. International Journal of Population Data Science. 5: 1345 |
| Akgün Ö, Dearle A, Kirby G, et al. (2020) Linking Scottish vital event records using family groups Historical Methods: a Journal of Quantitative and Interdisciplinary History. 53: 130-146 |
| Ranbaduge T, Christen P. (2020) A scalable privacy-preserving framework for temporal record linkage Knowledge and Information Systems. 62: 45-78 |
| Christen P, Ranbaduge T, Vatsalan D, et al. (2019) Precise and Fast Cryptanalysis for Bloom Filter Based Privacy-Preserving Record Linkage Ieee Transactions On Knowledge and Data Engineering. 31: 2164-2177 |
| Christen P, Ranbaduge T, Vatsalan D. (2018) An Introduction to DLforum - An online discussion forum for data linkage researchers and practitioners https://dmm.anu.edu.au/DLforum/. International Journal of Population Data Science. 3: 420 |
| Ranbaduge T, Vidanage A, Vaiwsri S, et al. (2018) Evaluating hardening techniques against cryptanalysis attacks on Bloom filter encodings for record linkage International Journal For Population Data Science. 3 |
| Hand DJ, Christen P. (2018) A note on using the F-measure for evaluating record linkage algorithms Statistics and Computing. 28: 539-547 |
| Christen P. (2017) Advanced methods for linking complex historical birth, death, marriage and census data International Journal For Population Data Science. 1 |
| Ranbaduge T, Vatsalan D, Randall S, et al. (2017) Evaluation of advanced techniques for multi-party privacy-preserving record linkage on real-world health databases International Journal For Population Data Science. 1 |
| Christen P, Gayler RW, Tran KN, et al. (2016) Automatic discovery of abnormal values in large textual databases Journal of Data and Information Quality. 7 |