Keith Bonawitz
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
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- Mobile Crowdsensing and Crowdsourcing
Papers in
-
- Privacy-Preserving Technologies in Data 3
- Topic Modeling 1
- Cryptography and Data Security 1
- Speech and dialogue systems 1
- Stochastic Gradient Optimization Techniques 1
- Internet Traffic Analysis and Secure E-voting 1
- Bayesian Modeling and Causal Inference 1
- Co-authors
- Daniel RamageH. Brendan McMahanVladimir IvanovSarvar PatelAntonio MarcedoneAaron SegalBen KreuterKarn Seth
- Journals
- International Conference on Machine Learning (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Keith Bonawitz
5 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 2.0k
- Computer Science Applications 259
- Health Informatics 42
- Information Systems 243
- Computer Networks and Communications 237
Countries citing papers authored by Keith Bonawitz
This map shows the geographic impact of Keith Bonawitz'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 Keith Bonawitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keith Bonawitz more than expected).
Fields of papers citing papers by Keith Bonawitz
This network shows the impact of papers produced by Keith Bonawitz. 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 Keith Bonawitz. The network helps show where Keith Bonawitz may publish in the future.
Co-authors
The 16 scholars most cited alongside Keith Bonawitz, 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 | Context Aware Local Differential Privacy | 2020 | 1 |
| 2 | Practical Secure Aggregation for Privacy-Preserving Machine Learning Hit paper breakdown → | 2017 | 1882 |
| 3 | Discrete distribution estimation under local privacy | 2016 | 81 |
| 4 | 2012 | 199 | |
| 5 | 2003 | 1 |
About Keith Bonawitz
Keith Bonawitz is a scholar working on Artificial Intelligence, Statistics and Probability, Computer Networks and Communications, Epidemiology and Sociology and Political Science, having authored 5 papers that have together received 2.2k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (3 papers), Topic Modeling (1 paper), Cryptography and Data Security (1 paper), Speech and dialogue systems (1 paper), Stochastic Gradient Optimization Techniques (1 paper), Internet Traffic Analysis and Secure E-voting (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Privacy, Security, and Data Protection (1 paper). The work is most often cited by research in Artificial Intelligence (2.0k citations), Computer Science Applications (259 citations), Health Informatics (42 citations), Information Systems (243 citations) and Computer Networks and Communications (237 citations). Keith Bonawitz has collaborated with scholars based in United States. Frequent co-authors include Daniel Ramage, H. Brendan McMahan, Vladimir Ivanov, Sarvar Patel, Antonio Marcedone, Aaron Segal, Ben Kreuter, Karn Seth, Vikash K. Mansinghka and Joshua B. Tenenbaum. Their work appears in journals such as International Conference on Machine Learning and arXiv (Cornell University).
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.