Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Raviv, Netanel"'
Minimum storage regenerating (MSR) codes are a class of maximum distance separable (MDS) array codes capable of repairing any single failed node by downloading the minimum amount of information from each of the helper nodes. However, MSR codes requir
Externí odkaz:
http://arxiv.org/abs/2408.16584
Autor:
Wang, Canran, Raviv, Netanel
We consider the problem of encoding information in a system of N=K+R processors that operate in a decentralized manner, i.e., without a central processor which orchestrates the operation. The system involves K source processors, each holding some dat
Externí odkaz:
http://arxiv.org/abs/2408.15203
We introduce a novel privacy notion of ($\epsilon, \delta$)-confounding privacy that generalizes both differential privacy and Pufferfish privacy. In differential privacy, sensitive information is contained in the dataset while in Pufferfish privacy,
Externí odkaz:
http://arxiv.org/abs/2408.12010
Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving delegation of data to a service provider, who may further delegate the computatio
Externí odkaz:
http://arxiv.org/abs/2405.05567
Given a real dataset and a computation family, we wish to encode and store the dataset in a distributed system so that any computation from the family can be performed by accessing a small number of nodes. In this work, we focus on the families of li
Externí odkaz:
http://arxiv.org/abs/2405.05845
Autor:
Wang, Canran, Wang, Jinwen, Zhou, Mi, Pham, Vinh, Hao, Senyue, Zhou, Chao, Zhang, Ning, Raviv, Netanel
The prevalence of 3D printing poses a significant risk to public safety, as any individual with internet access and a commodity printer is able to produce untraceable firearms, keys, counterfeit products, etc. To aid government authorities in combati
Externí odkaz:
http://arxiv.org/abs/2403.04918
Autor:
Raviv, Netanel
Hyperdimensional Computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors, and has a promising potential in applications ranging from machine learning to neuromorphic computing. One
Externí odkaz:
http://arxiv.org/abs/2403.03278
Mutual information between two random variables is a well-studied notion, whose understanding is fairly complete. Mutual information between one random variable and a pair of other random variables, however, is a far more involved notion. Specificall
Externí odkaz:
http://arxiv.org/abs/2402.03554
A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. Private Inference has been extensively e
Externí odkaz:
http://arxiv.org/abs/2311.13686
Feature extraction and selection at the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type orthogonalization process over function spaces to detect and map
Externí odkaz:
http://arxiv.org/abs/2311.09386