Collusion-secure fingerprinting for digital data

555 indexed citations

Abstract

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About

This paper, published in 1998, received 555 indexed citations. Written by Dan Boneh and James M. Shaw covering the research area of Information Systems, Signal Processing and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (475 citations), Artificial Intelligence (191 citations) and Information Systems (111 citations). Published in IEEE Transactions on Information Theory.

Countries where authors are citing Collusion-secure fingerprinting for digital data

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Citations

This map shows the geographic impact of Collusion-secure fingerprinting for digital data. 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 Collusion-secure fingerprinting for digital data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Collusion-secure fingerprinting for digital data more than expected).

Fields of papers citing Collusion-secure fingerprinting for digital data

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Collusion-secure fingerprinting for digital data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Collusion-secure fingerprinting for digital data.

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/10.1109/18.705568.

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