Marco Stronati
Impact in
- Health Informatics top 1%
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Anomaly Detection Techniques and Applications
Papers in
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- Mobile Crowdsensing and Crowdsourcing 2
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- Privacy-Preserving Technologies in Data 5
- Cryptography and Data Security 3
- Logic, programming, and type systems 1
- Bayesian Modeling and Causal Inference 1
- Security and Verification in Computing 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Vitaly ShmatikovReza ShokriCongzheng SongCatuscia PalamidessiKonstantinos ChatzikokolakisArthur Azevedo de AmorimCătălin HriţcuRoberto Blanco
- Journals
- SHILAP Revista de lepidopterología (2 papers)arXiv (Cornell University) (1 paper)National University of Singapore (1 paper)
In The Last Decade
Marco Stronati
6 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Health Informatics 112
- Artificial Intelligence 2.0k
- Computer Science Applications 180
- Signal Processing 163
- Safety Research 100
Countries citing papers authored by Marco Stronati
This map shows the geographic impact of Marco Stronati'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 Marco Stronati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Stronati more than expected).
Fields of papers citing papers by Marco Stronati
This network shows the impact of papers produced by Marco Stronati. 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 Marco Stronati. The network helps show where Marco Stronati may publish in the future.
Co-authors
The 10 scholars most cited alongside Marco Stronati, 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 | 2018 | 15 | |
| 2 | Membership Inference Attacks Against Machine Learning Models Hit paper breakdown → | 2017 | 2137 |
| 3 | 2015 | 21 | |
| 4 | 2015 | 57 | |
| 5 | A Predictive Differentially-Private Mechanism for Location Privacy. | 2013 | 4 |
| 6 | 2012 | 16 |
About Marco Stronati
Marco Stronati is a scholar working on Computer Science Applications, Artificial Intelligence, Signal Processing, Sociology and Political Science and Computer Networks and Communications, having authored 6 papers that have together received 2.3k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (5 papers), Cryptography and Data Security (3 papers), Privacy, Security, and Data Protection (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Logic, programming, and type systems (1 paper), Bayesian Modeling and Causal Inference (1 paper), Security and Verification in Computing (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (112 citations), Artificial Intelligence (2.0k citations), Computer Science Applications (180 citations), Signal Processing (163 citations) and Safety Research (100 citations). Marco Stronati has collaborated with scholars based in France, India and Italy. Frequent co-authors include Vitaly Shmatikov, Reza Shokri, Congzheng Song, Catuscia Palamidessi, Konstantinos Chatzikokolakis, Arthur Azevedo de Amorim, Cătălin Hriţcu, Roberto Blanco, Andrew Tolmach and Benjamin C. Pierce. Their work appears in journals such as SHILAP Revista de lepidopterología, arXiv (Cornell University) and National University of Singapore.
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.