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.
AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation [link]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}
}

Downloads: 0