Antonio Marcedone
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
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- Cryptography and Data Security 4
- Privacy-Preserving Technologies in Data 1
- Stochastic Gradient Optimization Techniques 1
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- Complexity and Algorithms in Graphs 2
- Co-authors
- Aaron Segal (1 shared paper)Keith Bonawitz (1 shared paper)Sarvar Patel (1 shared paper)Vladimir Ivanov (1 shared paper)Daniel Ramage (1 shared paper)Karn Seth (1 shared paper)H. Brendan McMahan (1 shared paper)Ben Kreuter (1 shared paper)
- Journals
- IACR Cryptology ePrint Archive (1 paper)
- Partner nations
- United StatesItalyIsrael
In The Last Decade
Antonio Marcedone
4 papers receiving 1.8k citations
Antonio Marcedone's Hit Papers
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 1.8k
- Computer Science Applications 234
- Health Informatics 42
- Information Systems 227
- Computer Networks and Communications 200
Countries citing papers authored by Antonio Marcedone
This map shows the geographic impact of Antonio Marcedone'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 Antonio Marcedone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antonio Marcedone more than expected).
Fields of papers citing papers by Antonio Marcedone
This network shows the impact of papers produced by Antonio Marcedone. 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 Antonio Marcedone. The network helps show where Antonio Marcedone may publish in the future.
Co-authors
The 14 scholars most cited alongside Antonio Marcedone, 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 | 2019 | 10 | |
| 3 | Obfuscation ==> (IND-CPA Security =/=> Circular Security). | 2013 | 3 |
| 4 | Authenticating Computation on Groups: New Homomorphic Primitives and Applications | 2014 | 1 |
About Antonio Marcedone
Antonio Marcedone is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Geometry and Topology and Computer Vision and Pattern Recognition, having authored 4 papers that have together received 1.9k indexed citations. Recurring topics across this work include Cryptography and Data Security (4 papers), Complexity and Algorithms in Graphs (2 papers), Privacy-Preserving Technologies in Data (1 paper), Stochastic Gradient Optimization Techniques (1 paper), Geometric and Algebraic Topology (1 paper), graph theory and CDMA systems (1 paper), Blockchain Technology Applications and Security (1 paper) and Chaos-based Image/Signal Encryption (1 paper). The work is most often cited by research in Artificial Intelligence (1.8k citations), Computer Science Applications (234 citations), Health Informatics (42 citations), Information Systems (227 citations) and Computer Networks and Communications (200 citations). Antonio Marcedone has collaborated with scholars based in United States, Italy and Israel. Frequent co-authors include Aaron Segal, Keith Bonawitz, Sarvar Patel, Vladimir Ivanov, Daniel Ramage, Karn Seth, H. Brendan McMahan, Ben Kreuter, Muthuramakrishnan Venkitasubramaniam and Carmit Hazay. Their work appears in journals such as IACR Cryptology ePrint Archive.
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.