48 citations · 108 across the 10 of their papers we have counts for
11 papers
A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
Najeeb Jebreel, David Sánchez, Josep Domingo-Ferrer
Membership inference attacks (MIAs) aim to determine whether a data sample was included in a machine learning (ML) model's training set and have become the de facto standard for me…
Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
Najeeb Jebreel, Mona Khalil, David Sánchez +1
Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely…
Membership Inference Attacks Beyond Overfitting
Mona Khalil, Alberto Blanco-Justicia, Najeeb Jebreel +1
Membership inference attacks (MIAs) against machine learning (ML) models aim to determine whether a given data point was part of the model training data. These attacks may pose sig…
Efficient Unlearning with Privacy Guarantees
Josep Domingo-Ferrer, Najeeb Jebreel, David Sánchez
Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML)…
DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs
Tamim Al Mahmud, Najeeb Jebreel, Josep Domingo-Ferrer +1
Large language models (LLMs) have recently revolutionized language processing tasks but have also brought ethical and legal issues. LLMs have a tendency to memorize potentially pri…
Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares +4
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations fo…
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
David Sánchez, Najeeb Jebreel, Krishnamurty Muralidhar +2
The threat of reconstruction attacks has led the U.S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank s…
Defending Against Backdoor Attacks by Layer-wise Feature Analysis
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, Yiming Li
Training deep neural networks (DNNs) usually requires massive training data and computational resources. Users who cannot afford this may prefer to outsource training to a third pa…
Defending against the Label-flipping Attack in Federated Learning
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, David Sánchez +1
Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in th…
FL-Defender: Combating Targeted Attacks in Federated Learning
Najeeb Jebreel, Josep Domingo-Ferrer
Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more a…
Enhanced Security and Privacy via Fragmented Federated Learning
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, Alberto Blanco-Justicia +1
In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconcili…