ﻻ يوجد ملخص باللغة العربية
E-voting systems are a powerful technology for improving democracy. Unfortunately, prior voting systems have single points-of-failure, which may compromise availability, privacy, or integrity of the election results. We present the design, implementation, security analysis, and evaluation of the D-DEMOS suite of distributed, privacy-preserving, and end-to-end verifiable e-voting systems. We present two systems: one asynchronous and one with minimal timing assumptions but better performance. Our systems include a distributed vote collection subsystem that does not require cryptographic operations on behalf of the voter. We also include a distributed, replicated and fault-tolerant Bulletin Board component, that stores all necessary election-related information, and allows any party to read and verify the complete election process. Finally, we incorporate trustees, who control result production while guaranteeing privacy and end-to-end-verifiability as long as their strong majority is honest. Our suite of e-voting systems are the first whose voting operation is human verifiable, i.e., a voter can vote over the web, even when her web client stack is potentially unsafe, without sacrificing her privacy, and still be assured her vote was recorded as cast. Additionally, a voter can outsource election auditing to third parties, still without sacrificing privacy. We provide a model and security analysis of the systems, implement complete prototypes, measure their performance experimentally, and demonstrate their ability to handle large-scale elections. Finally, we demonstrate the performance trade-offs between the t
This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, dif
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design SPINDLE (Scalabl
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and deep CNNs, however, require a large amount of computing resources and
In this paper, we study the problem of summation evaluation of secrets. The secrets are distributed over a network of nodes that form a ring graph. Privacy-preserving iterative protocols for computing the sum of the secrets are proposed, which are co
The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that count