Zobrazeno 1 - 10
of 508
pro vyhledávání: '"Khan, Tanveer A."'
Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning (PPML) methods
Externí odkaz:
http://arxiv.org/abs/2409.06422
The popularity of Machine Learning (ML) makes the privacy of sensitive data more imperative than ever. Collaborative learning techniques like Split Learning (SL) aim to protect client data while enhancing ML processes. Though promising, SL has been p
Externí odkaz:
http://arxiv.org/abs/2404.09265
Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial computing power and are only as powerful as the data utilized. Due to high
Externí odkaz:
http://arxiv.org/abs/2403.03592
$\mathbb{X}$ (formerly Twitter) is a prominent online social media platform that plays an important role in sharing information making the content generated on this platform a valuable source of information. Ensuring trust on $\mathbb{X}$ is essentia
Externí odkaz:
http://arxiv.org/abs/2402.02066
Machine Learning (ML) has emerged as one of data science's most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns owing to the increasing number of malicious attacks targeting ML models
Externí odkaz:
http://arxiv.org/abs/2401.14840
Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the ma
Externí odkaz:
http://arxiv.org/abs/2309.10517
Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the ma
Externí odkaz:
http://arxiv.org/abs/2309.08697
Autor:
Khan, Tanveer, Michalas, Antonis
Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption and implementation of cloud-based services. As a result, various solutions have been proposed in which the machine learning model
Externí odkaz:
http://arxiv.org/abs/2309.08190
The popularity of Deep Learning (DL) makes the privacy of sensitive data more imperative than ever. As a result, various privacy-preserving techniques have been implemented to preserve user data privacy in DL. Among various privacy-preserving techniq
Externí odkaz:
http://arxiv.org/abs/2308.15783
Split Learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the ma
Externí odkaz:
http://arxiv.org/abs/2301.08778