Elisa Bertino

About

Elisa Bertino is a leading researcher in cybersecurity, policy-based management, and edge computing, with a focus on securing autonomous systems and IoT environments. Her work addresses critical challenges in access control, federated learning, and efficient AI deployment. Notably, her 2019 paper on pruning deep convolutional neural networks for edge computing in infrastructure assessment has garnered 99 citations, demonstrating significant impact in enabling real-time condition monitoring using resource-constrained devices like drones and robots. She has also advanced policy analysis methods, with a 2019 paper receiving 51 citations, providing tools for managing large-scale systems with autonomous devices. More recently, Bertino introduced FLAP, a federated learning framework for attribute-based access control policies, which has already attracted attention with 10 citations since 2023. Her contributions extend to next-generation wireless networking, where she has outlined computing research challenges for 5G and beyond. Through her innovative approaches to security and efficiency in distributed systems, Bertino continues to shape the future of trustworthy autonomous collaboration and infrastructure monitoring.

Research Focus

Key Achievements

Top Papers

1. 1
   Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

   99 citations · 2019
2. 2
   Methods and Tools for Policy Analysis

   51 citations · 2019
3. 3
   FLAP - A Federated Learning Framework for Attribute-based Access Control Policies

   10 citations · 2023
4. 4
   Computing Research Challenges in Next Generation Wireless Networking

   9 citations · 2021
5. 5
   FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies

   3 citations · 2020
6. 6
   Pruning Deep Neural Networks for Efficient Edge Computing in Internet of Things: A Structural Health Monitoring Case Study

   2 citations · 2019

Key Collaborators

Contact & Links