Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Xu, Runhua"'
A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exchange. However, the recent Tron-Steem takeover incident poses a signifi
Externí odkaz:
http://arxiv.org/abs/2407.17825
How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting Governance
Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Stee
Externí odkaz:
http://arxiv.org/abs/2310.18596
Publikováno v:
ACM BSCI 2023
Voting mechanisms play a crucial role in decentralized governance of blockchain systems. Liquid democracy, also known as delegative voting, allows voters to vote directly or delegate their voting power to others, thereby contributing to the resolutio
Externí odkaz:
http://arxiv.org/abs/2309.01090
Publikováno v:
IEEE BigDataSecurity 2023
Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior research has primarily examined decentralization in blockchains emplo
Externí odkaz:
http://arxiv.org/abs/2306.05788
Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation
Externí odkaz:
http://arxiv.org/abs/2207.07779
Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model relies on a large volume of training data and high-powered computational resources. Such a need for and the use of huge
Externí odkaz:
http://arxiv.org/abs/2108.04417
Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are
Externí odkaz:
http://arxiv.org/abs/2103.03918
Publikováno v:
IEEE TDSC 2022
Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous
Externí odkaz:
http://arxiv.org/abs/2102.01249
Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously ma
Externí odkaz:
http://arxiv.org/abs/2102.00177
Training a machine learning model over an encrypted dataset is an existing promising approach to address the privacy-preserving machine learning task, however, it is extremely challenging to efficiently train a deep neural network (DNN) model over en
Externí odkaz:
http://arxiv.org/abs/2012.10547