Reza Shokri
About
In The Last Decade
Reza Shokri
58 papers receiving 6.7k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Artificial Intelligence 6.1k
- Sociology and Political Science 1.2k
- Computer Science Applications 865
- Electrical and Electronic Engineering 837
- Computer Networks and Communications 698
Countries citing papers authored by Reza Shokri
This map shows the geographic impact of Reza Shokri'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 Reza Shokri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Reza Shokri more than expected).
Fields of papers citing papers by Reza Shokri
This network shows the impact of papers produced by Reza Shokri. 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 Reza Shokri. The network helps show where Reza Shokri may publish in the future.
Co-authorship network of co-authors of Reza Shokri
This figure shows the co-authorship network connecting the top 25 collaborators of Reza Shokri. A scholar is included among the top collaborators of Reza Shokri based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Reza Shokri. Reza Shokri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 34 | |
| 3 | Epione: Lightweight Contact Tracing with Strong Privacy | 3 |
| 4 | 1 | |
| 5 | 44 | |
| 6 | Privacy Risks of Explaining Machine Learning Models. | 7 |
| 7 | Ultimate Power of Inference Attacks: Privacy Risks of Learning High-Dimensional Graphical Models | 2 |
| 8 | Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. | 51 |
| 9 | 100 | |
| 10 | 152 | |
| 11 | Privacy-Preserving Deep Learning breakdown → | 1207 |
| 12 | Optimal User-Centric Data Obfuscation | 6 |
| 13 | Privacy Games: Optimal Protection Mechanism Design for Bayesian and Differential Privacy. | 3 |
| 14 | 47 | |
| 15 | 37 | |
| 16 | 2 | |
| 17 | Quantifying Location Privacy: The Case of Sporadic Location Exposure | 1 |
| 18 | MobiCrowd: A Collaborative Location-Privacy Preserving Mobile Proxy | 3 |
| 19 | 1 | |
| 20 | 3 |
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.