CryptoNets: applying neural networks to encrypted data with high throughput and accuracy
Impact in
Classified as
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w7675009 →Countries where authors are citing CryptoNets: applying neural networks to encrypted data with high throughput and accuracy
This map shows the geographic impact of CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. 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 CryptoNets: applying neural networks to encrypted data with high throughput and accuracy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites CryptoNets: applying neural networks to encrypted data with high throughput and accuracy more than expected).
Fields of papers citing CryptoNets: applying neural networks to encrypted data with high throughput and accuracy
This network shows the impact of CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the CryptoNets: applying neural networks to encrypted data with high throughput and accuracy.
About CryptoNets: applying neural networks to encrypted data with high throughput and accuracy
This paper, published in 2016, received 766 indexed citations . Written by Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig and John Wernsing covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (680 citations), Computer Vision and Pattern Recognition (164 citations), Information Systems (121 citations), Computational Theory and Mathematics (65 citations) and Computer Networks and Communications (57 citations). Published in International Conference on Machine Learning.
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.
This paper is also available at doi.org/w7675009.