Karn Seth
Impact in
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Adversarial Robustness in Machine Learning
- Internet Traffic Analysis and Secure E-voting
-
- Mobile Crowdsensing and Crowdsourcing
Papers in
-
- Cryptography and Data Security 6
- Privacy-Preserving Technologies in Data 3
- Cryptographic Implementations and Security 3
- Quantum Computing Algorithms and Architecture 1
- Security and Verification in Computing 1
-
- Complexity and Algorithms in Graphs 1
- Computability, Logic, AI Algorithms 1
- Computational Drug Discovery Methods 1
- Co-authors
- Sarvar Patel (2 shared papers)Ben Kreuter (2 shared papers)Aaron Segal (1 shared paper)Keith Bonawitz (1 shared paper)Vladimir Ivanov (1 shared paper)Antonio Marcedone (1 shared paper)Daniel Ramage (1 shared paper)H. Brendan McMahan (1 shared paper)
- Partner nations
- United StatesSwedenTaiwan
In The Last Decade
Karn Seth
6 papers receiving 1.9k citations
Karn Seth's Hit Papers
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 1.8k
- Computer Science Applications 235
- Health Informatics 42
- Information Systems 239
- Computer Networks and Communications 208
Countries citing papers authored by Karn Seth
This map shows the geographic impact of Karn Seth's research. 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 Karn Seth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karn Seth more than expected).
Fields of papers citing papers by Karn Seth
This network shows the impact of papers produced by Karn Seth. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Karn Seth. The network helps show where Karn Seth may publish in the future.
Co-authors
The 17 scholars most cited alongside Karn Seth, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Practical Secure Aggregation for Privacy-Preserving Machine Learning Hit paper breakdown → | 2017 | 1882 |
| 2 | 2020 | 50 | |
| 3 | 2016 | 4 | |
| 4 | 2013 | 4 | |
| 5 | 2024 | 2 | |
| 6 | 2016 | 1 | |
| 7 | Block Sensitivity versus Sensitivity | 2010 | 0 |
About Karn Seth
Karn Seth is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 7 papers that have together received 1.9k indexed citations. Recurring topics across this work include Cryptography and Data Security (6 papers), Privacy-Preserving Technologies in Data (3 papers), Cryptographic Implementations and Security (3 papers), Complexity and Algorithms in Graphs (1 paper), Quantum Computing Algorithms and Architecture (1 paper), Computability, Logic, AI Algorithms (1 paper), Computational Drug Discovery Methods (1 paper) and Security and Verification in Computing (1 paper). The work is most often cited by research in Artificial Intelligence (1.8k citations), Computer Science Applications (235 citations), Health Informatics (42 citations), Information Systems (239 citations) and Computer Networks and Communications (208 citations). Karn Seth has collaborated with scholars based in United States, Sweden and Taiwan. Frequent co-authors include Sarvar Patel, Ben Kreuter, Aaron Segal, Keith Bonawitz, Vladimir Ivanov, Antonio Marcedone, Daniel Ramage, H. Brendan McMahan, Mihaela Ion and Moti Yung. Their work appears in journals such as Algorithmica and SIAM Journal on Computing.
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.