Benny Pinkas

Benny Pinkas is affiliated with Bar-Ilan University in Israel and has contributed extensively to research in computer science, particularly in the areas of cryptography and data security as well as privacy-preserving technologies in data.

Their research spans several fields, with a strong focus on artificial intelligence, computational theory and mathematics, and information systems. They have also published work related to transportation and computer networks and communications.

Key topics in their work include:

• Cryptography and Data Security
• Privacy-Preserving Technologies in Data
• Complexity and Algorithms in Graphs
• Human Mobility and Location-Based Analysis
• Internet Traffic Analysis and Secure E-voting
• Cloud Data Security Solutions
• Coding theory and cryptography

Recent papers by Benny Pinkas cover a range of subjects related to secure computation and privacy:

• "Efficient Secure Three-Party Sorting with Applications to Data Analysis and Heavy Hitters," 2022, Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
• "Privacy-Preserving Epidemiological Modeling on Mobile Graphs," 2022, arXiv (Cornell University)
• "ScionFL: Efficient and Robust Secure Quantized Aggregation," 2022, arXiv (Cornell University)
• "Privacy-Preserving Epidemiological Modeling on Mobile Graphs," 2025, IEEE Transactions on Information Forensics and Security
• "Poster," 2022, Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security

Frequent co-authors in their publications include Thomas Schneider, Ajith Suresh, Avishay Yanai, Helen Möllering, and Marco Holz. This collaborative network has contributed to multiple works, indicating ongoing joint efforts within the field.

Benny Pinkas has published multiple papers in venues such as:

• Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
• arXiv (Cornell University)
• IEEE Transactions on Information Forensics and Security

The body of research represents a focus on secure and privacy-conscious approaches within computer science, especially addressing challenges in data security, secure computations, and epidemiological modeling, alongside contributions to theoretical and applied aspects of algorithms, artificial intelligence, and communication networks.