Majid Mollaeefar
Cybersecurity | Privacy Enthusiast.
About Me
I am a highly skilled and experienced cybersecurity researcher with a focus on risk management, IT security, risk assessment and analysis, data protection, and compliance regulations. My extensive research background has equipped me with strong analytical skills, including the ability to design and conduct studies, analyze data, and present findings to a wide range of audiences. I hold a Ph.D. degree in Computer science and Systems engineering from the University of Genova, Italy.
Education
Ph.D. Cybersecurity Posture
University of Genova, Genova, Italy
Nov 2018 - Nov 2022
Dissertation: “Automating the Quantification and Mitigation of Risks for Multiple Stakeholders” (pdf)
Advisor: Prof. Silvio Ranise
Topics of Interest
Publications
| # | Title | Year |
|---|---|---|
| A Risk-based Approach to Trustworthy AI Systems for Judicial Procedures.
Majid Mollaeefar, Eleonora Marchesini, Roberto Carbone, and Silvio Ranise.
4th Ital-AI Workshop AI Responsabile e Affidabile. (pdf)
|
2024 | |
| The DPIA of an Enterprise Contact Tracing Solution: Lessons Learned at the Crossroads of Cybersecurity and Data Protection
Majid Mollaeefar, Roberto Carbone, and Silvio Ranise.
To be submitted. Unpublished version (pdf)
|
2024 | |
| Identifying and Quantifying Trade-offs in Multi-Stakeholder Risk Evaluation with Applications to the Data Protection Impact Assessment of the GDPR
Majid Mollaeefar, and Silvio Ranise.
Journal of Computers & Securit. (pdf)
|
2023 | |
| Multi-Stakeholder Cybersecurity Risk Assessment for Data
Protection.
Majid Mollaeefar, Alberto Siena, and Silvio Ranise.
17th International Conference on Security and Cryptography (SECRYPT 2020). (pdf)
|
2020 | |
| 3 | A novel encryption scheme for colored image based on high level chaotic
maps.
Majid Mollaeefar, Amir Sharif, and Mahboubeh Nazari.
Journal of Multimedia Tools and Applications
|
2017 |
| 4 | A novel method for digital image steganography based on a new
three-dimensional chaotic map.
Sharif, Amir, Majid Mollaeefar, and Mahboubeh Nazari.
Journal of Multimedia Tools and Applications
|
2017 |
| 5 | An improved method for digital image fragile watermarking based on chaotic
maps.
Nazari, Mahboubeh, Amir Sharif, and Majid Mollaeefar.
Journal of Multimedia Tools and Applications
|
2017 |
| 6 | A novel method for image encryption using chaotic maps.
Amir Sharif, Majid Mollaeefar, M. Habibi, and M. Nazari.
3rd international conference on applied research in computer and information
technology.
|
2016 |
| 7 | An improved method for image encryption based on high level chaotic maps
and improved gravity model.
Majid Mollaeefar, Amir Sharif, M. Habibi, and Mahboubeh Nazari.
International Congress on Technology, Communication and Knowledge (ICTCK).
|
2015 |
Developed Tools
Multi-Stakeholder Risk Assessment Tool
Description: It is a risk assessment tool that enables risk analysts to perform a risk evaluation in a multi-stakeholder manner for a given system. The tool has a two-fold purpose:
- Evaluate and quantify risk levels for all involved stakeholders
- Solving the risk minimization problem, which is a multi-objective optimization problem
Program Language: This tool is written in Java and partially in Scala, it works with JSON files, all inputs and outputs.
Access: Publicly available. Link
Privacy Assessment GPT
Description: It is an AI-powered tool aiming to support privacy threat modeling and risk assessment by leveraging LLMs. Some features of the tool are:
- Works with different LLMs such as Open AI and Mistral
- Identify privacy threats based on LINDDUN threat categories together with potential consequences
- Generate threat scenarios and propose controls for the identified threats
- Generate different mitigation levels and interact the user for risk assessment in the form of generating a questionnaire
- and many more
Program Language: This tool is written in Python.
Access: Not publicly available yet. It is an ongoing project and if you are intersted, please contact me for more details .
Supervised Internship & Theses
Current Students:
|
Exploring LLMs for privacy threats assessment
Andrea Bissoli (Internship, University of Trento, July 2024)
|
|
|
Transferability of Adversarial Machine Learning Attacks
Stefano Camposilvan (Bachelor's Thesis, University of Trento, May 2024)
|
Past Students:
|
Towards Risk Assessment of Adversarial Machine Learning
Mattia Bressan (Bachelor's Thesis, University of Trento, November 2023)
|
Contact
Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.
Email: m[surname]@fbk.eu