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of 107
pro vyhledávání: '"Frikha, Ahmed"'
In this work, we introduce PII-Scope, a comprehensive benchmark designed to evaluate state-of-the-art methodologies for PII extraction attacks targeting LLMs across diverse threat settings. Our study provides a deeper understanding of these attacks b
Externí odkaz:
http://arxiv.org/abs/2410.06704
Autor:
Frikha, Ahmed, Walha, Nassim, Mendes, Ricardo, Nakka, Krishna Kanth, Jiang, Xue, Zhou, Xuebing
This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private data of another data owner entity, in a way that ensures the confidentia
Externí odkaz:
http://arxiv.org/abs/2407.02960
Autor:
Frikha, Ahmed, Walha, Nassim, Nakka, Krishna Kanth, Mendes, Ricardo, Jiang, Xue, Zhou, Xuebing
In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics. We propose IncogniText, a tech
Externí odkaz:
http://arxiv.org/abs/2407.02956
The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output person
Externí odkaz:
http://arxiv.org/abs/2407.02943
Autor:
Zhang, Yao, Chen, Haokun, Frikha, Ahmed, Yang, Yezi, Krompass, Denis, Zhang, Gengyuan, Gu, Jindong, Tresp, Volker
Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language questions, as well as commonsense reasoning over the information cont
Externí odkaz:
http://arxiv.org/abs/2211.10567
Autor:
Majeed, Basima Abbood1 basimaabboodmajeed2022@gmail.com, Frikha, Ahmed2 ahmed.frikha@isgis.usf.tn
Publikováno v:
International Journal of Professional Business Review (JPBReview). 2024, Vol. 9 Issue 9, p1-20. 20p.
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence preserve data privacy. Real-world applications usually involve a distr
Externí odkaz:
http://arxiv.org/abs/2205.14900
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into a single m
Externí odkaz:
http://arxiv.org/abs/2110.04545
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source d
Externí odkaz:
http://arxiv.org/abs/2109.04320
Publikováno v:
In Procedia Computer Science 2024 232:2157-2166