Briland Hitaj
Impact in
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
- Stochastic Gradient Optimization Techniques
- Internet Traffic Analysis and Secure E-voting
- Health Informatics top 5%
Papers in
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- Adversarial Robustness in Machine Learning 3
- Privacy-Preserving Technologies in Data 2
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- User Authentication and Security Systems 1
- Digital and Cyber Forensics 1
- Co-authors
- Giuseppe Ateniese (1 shared paper)Fernando Pérez‐Cruz (2 shared papers)Luigi V. Mancini (3 shared papers)Sushil Jajodia (1 shared paper)Deborah Shands (1 shared paper)Fabio De Gaspari (1 shared paper)Ulf Lindqvist (1 shared paper)Timothy J. Ellis (1 shared paper)
- Journals
- Computer Science Review (1 paper)IEEE Transactions on Dependable and Secure Computing (1 paper)IRIS Research product catalog (Sapienza University of Rome) (3 papers)
- Partner nations
- United StatesItalySwitzerland
In The Last Decade
Briland Hitaj
4 papers receiving 809 citations
Briland Hitaj's Hit Papers
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 764
- Health Informatics 25
- Computer Science Applications 70
- Computer Vision and Pattern Recognition 96
- Signal Processing 47
Countries citing papers authored by Briland Hitaj
This map shows the geographic impact of Briland Hitaj'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 Briland Hitaj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Briland Hitaj more than expected).
Fields of papers citing papers by Briland Hitaj
This network shows the impact of papers produced by Briland Hitaj. 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 Briland Hitaj. The network helps show where Briland Hitaj may publish in the future.
Co-authors
The 8 scholars most cited alongside Briland Hitaj, 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 | Deep Models Under the GAN Hit paper breakdown → | 2017 | 803 |
| 2 | 2019 | 16 | |
| 3 | 2021 | 10 | |
| 4 | 2025 | 2 | |
| 5 | 2023 | 0 | |
| 6 | 2026 | 0 |
About Briland Hitaj
Briland Hitaj is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 6 papers that have together received 831 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Privacy-Preserving Technologies in Data (2 papers), Advanced Neural Network Applications (2 papers), Advanced Malware Detection Techniques (2 papers), Infrastructure Resilience and Vulnerability Analysis (1 paper), User Authentication and Security Systems (1 paper), Network Security and Intrusion Detection (1 paper) and Digital and Cyber Forensics (1 paper). The work is most often cited by research in Artificial Intelligence (764 citations), Health Informatics (25 citations), Computer Science Applications (70 citations), Computer Vision and Pattern Recognition (96 citations) and Signal Processing (47 citations). Briland Hitaj has collaborated with scholars based in United States, Italy and Switzerland. Frequent co-authors include Giuseppe Ateniese, Fernando Pérez‐Cruz, Luigi V. Mancini, Sushil Jajodia, Deborah Shands, Fabio De Gaspari, Ulf Lindqvist and Timothy J. Ellis. Their work appears in journals such as Computer Science Review, IEEE Transactions on Dependable and Secure Computing and IRIS Research product catalog (Sapienza University of Rome).
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.