Researchers including Dr. Najwa Aaraj developed ML-FEED, a new exploit detection framework using pattern-based techniques. The model is 70x faster than LSTMs and 75,000x faster than Transformers in exploit detection tasks, while also being slightly more accurate. The "ML-FEED" paper won best paper at the 2022 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications. Why it matters: This research enables more efficient real-time security applications and highlights growing AI expertise in the Arab world.
Researchers from MBZUAI introduce Forget-MI, a machine unlearning method tailored for multimodal medical data, enhancing privacy by removing specific patient data from AI models. Forget-MI utilizes loss functions and perturbation techniques to unlearn both unimodal and joint data representations. The method demonstrates superior performance in reducing Membership Inference Attacks and improving data removal compared to existing techniques, while preserving overall model performance and enabling data forgetting.
MBZUAI researchers developed FeSViBS, a new federated split learning technique for vision transformers that addresses data scarcity and privacy concerns in healthcare image classification. The method combines federated learning and split learning to train models collaboratively without sharing sensitive patient data directly. It overcomes limitations of traditional centralized training and vulnerabilities in federated learning. Why it matters: This approach enables the development of AI-powered healthcare applications while adhering to stringent data privacy regulations, unlocking the potential of machine learning in medical imaging.
A research paper by Fatima Al Nuaimi, Dr. Pietro Tedeschi, and Dr. Enrico Natalizio from the Autonomous Robotics Research Center (ARRC) has been published in IEEE Transactions on Industrial Informatics. The paper, titled “Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions”, examines vulnerabilities in UAV Remote ID systems. It identifies challenges for industry and academia in enhancing UAV security and privacy. Why it matters: The research highlights critical security and privacy considerations for the rapidly growing UAV sector in the region and globally.
KAUST Ph.D. student Mohammed Aljahdali received the Best Paper award at the International Conference on Federated Learning Technologies and Applications (FLTA) 2025 for his research on federated learning. His paper, "Flashback: Understanding and Mitigating Forgetting in Federated Learning," introduces an algorithm to help AI systems retain knowledge across diverse datasets while preserving privacy. Aljahdali's research, supervised by Professor Marco Canini, focuses on training machine learning models directly on user devices. Why it matters: This award recognizes the growing talent and impactful research emerging from Saudi universities in the field of privacy-preserving AI.
MBZUAI master's graduate Rohit Bharadwaj is pursuing a Ph.D. at the University of Edinburgh, following in the footsteps of Geoffrey Hinton. His research focuses on developing generative models, specifically diffusion models, to anonymize datasets while preserving utility, addressing GDPR compliance. He aims to balance privacy protection with the need for useful data in AI systems. Why it matters: This highlights the growing importance of MBZUAI as a feeder institution for top global AI research programs and the increasing focus on privacy-preserving AI technologies.
This article discusses a talk by Houda Elmimouni on designing technology with user values in mind, using privacy and robotic telepresence research as examples. The first study examines privacy practices, while the second focuses on values in robotic telepresence in classrooms. Elmimouni highlights the importance of aligning technology design with social values like privacy. Why it matters: The emphasis on user-centered design and social values provides insights applicable to AI development in the Middle East, where cultural context and ethical considerations are paramount.
A research talk was given on privacy and security issues in speech processing, highlighting the unique privacy challenges due to the biometric information embedded in speech. The talk covered the legal landscape, proposed solutions like cryptographic and hashing-based methods, and adversarial processing techniques. Dr. Bhiksha Raj from Carnegie Mellon University, an expert in speech and audio processing, delivered the talk. Why it matters: As speech-based interfaces become more prevalent in the Middle East, understanding and addressing the associated privacy risks is crucial for ethical AI development and deployment.
Researchers are exploring methods for evaluating the outcome of actions using off-policy observations where the context is noisy or anonymized. They employ proxy causal learning, using two noisy views of the context to recover the average causal effect of an action without explicitly modeling the hidden context. The implementation uses learned neural net representations for both action and context, and demonstrates outperformance compared to an autoencoder-based alternative. Why it matters: This research addresses a key challenge in applying AI in real-world scenarios where data privacy or bandwidth limitations necessitate working with noisy or anonymized data.
MBZUAI researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
MBZUAI Assistant Professor Samuel Horváth is researching federated learning to address the tension between data privacy and the predictive power of machine learning models. Federated learning trains models on decentralized data, keeping sensitive information on devices. Horváth's research focuses on designing algorithms that can efficiently train on distributed data while respecting user privacy. Why it matters: This work is crucial for advancing AI in sensitive domains like healthcare, where privacy regulations limit centralized data collection.
MBZUAI is hosting a webinar on September 1st featuring Professor Anil K. Jain to discuss AI research advances in biometrics, its applications, and challenges like user privacy. The webinar will highlight opportunities presented by new biometric and facial recognition systems and key application areas like airport security. The UAE's adoption of multi-biometric entry and exit programs in airports will also be discussed. Why it matters: As biometric technology sees increased adoption, this talk will help address concerns around reliability, security and accuracy of biometric recognition algorithms.
MBZUAI Assistant Professor Bin Gu is working on black-box optimization techniques, especially in the context of vertical federated learning. Gu's work, in collaboration with JD.com, aims to enhance data and model privacy in machine learning. He is also focused on large-scale optimization and spiking neural networks to bring machine automation closer to the way the human brain operates. Why it matters: This research contributes to advancements in privacy-preserving machine learning techniques relevant to sensitive sectors like finance and healthcare in the region.