AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation. Yao, Y., Ghai, T., Ravi, S., & Szekely, P. A. In Demartini, G., Zuccon, G., Culpepper, J. S., Huang, Z., & Tong, H., editors, CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, pages 2394–2403, 2021. ACM. Paper doi bibtex @inproceedings{DBLP:conf/cikm/YaoGRS21,
author = {Yixiang Yao and
Tanmay Ghai and
Srivatsan Ravi and
Pedro A. Szekely},
editor = {Gianluca Demartini and
Guido Zuccon and
J. Shane Culpepper and
Zi Huang and
Hanghang Tong},
title = {{AMPPERE:} {A} Universal Abstract Machine for Privacy-Preserving Entity
Resolution Evaluation},
booktitle = {{CIKM} '21: The 30th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Queensland, Australia, November
1 - 5, 2021},
pages = {2394--2403},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482318},
doi = {10.1145/3459637.3482318},
timestamp = {Tue, 02 Nov 2021 12:01:17 +0100},
biburl = {https://dblp.org/rec/conf/cikm/YaoGRS21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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