Related papers: Secure Linear Programming Using Privacy-Preserving…
This paper has been withdrawn by the corresponding author because the newest version is now published in Journal of Discrete Algorithms.
This paper was withdrawn by the author due to an edition's rights
This paper has been withdrawn by the authors due to an error.
This paper has been withdrawn by the author due to a crucial sign error.
In this work, we propose an outsourced Secure Multilayer Perceptron (SMLP) scheme where privacy and confidentiality of both the data and the model are ensured during the training and the classification phases. More clearly, this SMLP : i)…
This paper has been withdrawn by the authors, since it has been merged with Part I (ID 0802.3570)
Secure Multiparty Computation (SMC) allows parties to know the result of cooperative computation while preserving privacy of individual data. Secure sum computation is an important application of SMC. In our proposed protocols parties are…
In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all…
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication…
A modified version of this paper is under process and with a new title and abstract. Hence, this version of the article is completely withdrawn.
This paper has been withdrawn
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations.…
Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning…
This paper has been withdrawn by the author due to a crucial error in the formulation.
This paper has been withdrawn by the author. This paper is now obsolete. For a solution please see: arXiv:/1205.4265.
In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively using homomorphic encryption based on Paillier scheme. In the…
With the increasing emphasis on privacy regulations, such as GDPR, protecting individual privacy and ensuring compliance have become critical concerns for both individuals and organizations. Privacy-preserving machine learning (PPML) is an…
This paper presents a novel approach to classical linear regression, enabling model computation from data streams or in a distributed setting while preserving data privacy in federated environments. We extend this framework to generalized…
The paper is withdrawn. The proof has an error and it requires a different approach.
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where…