56,833 research outputs found
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks
Federated Learning (FL) enables a large number of users to jointly learn a
shared machine learning (ML) model, coordinated by a centralized server, where
the data is distributed across multiple devices. This approach enables the
server or users to train and learn an ML model using gradient descent, while
keeping all the training data on users' devices. We consider training an ML
model over a mobile network where user dropout is a common phenomenon. Although
federated learning was aimed at reducing data privacy risks, the ML model
privacy has not received much attention.
In this work, we present PrivFL, a privacy-preserving system for training
(predictive) linear and logistic regression models and oblivious predictions in
the federated setting, while guaranteeing data and model privacy as well as
ensuring robustness to users dropping out in the network. We design two
privacy-preserving protocols for training linear and logistic regression models
based on an additive homomorphic encryption (HE) scheme and an aggregation
protocol. Exploiting the training algorithm of federated learning, at the core
of our training protocols is a secure multiparty global gradient computation on
alive users' data. We analyze the security of our training protocols against
semi-honest adversaries. As long as the aggregation protocol is secure under
the aggregation privacy game and the additive HE scheme is semantically secure,
PrivFL guarantees the users' data privacy against the server, and the server's
regression model privacy against the users. We demonstrate the performance of
PrivFL on real-world datasets and show its applicability in the federated
learning system.Comment: In Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing
Security Workshop (CCSW'19
Secure Routing in Wireless Mesh Networks
Wireless mesh networks (WMNs) have emerged as a promising concept to meet the
challenges in next-generation networks such as providing flexible, adaptive,
and reconfigurable architecture while offering cost-effective solutions to the
service providers. Unlike traditional Wi-Fi networks, with each access point
(AP) connected to the wired network, in WMNs only a subset of the APs are
required to be connected to the wired network. The APs that are connected to
the wired network are called the Internet gateways (IGWs), while the APs that
do not have wired connections are called the mesh routers (MRs). The MRs are
connected to the IGWs using multi-hop communication. The IGWs provide access to
conventional clients and interconnect ad hoc, sensor, cellular, and other
networks to the Internet. However, most of the existing routing protocols for
WMNs are extensions of protocols originally designed for mobile ad hoc networks
(MANETs) and thus they perform sub-optimally. Moreover, most routing protocols
for WMNs are designed without security issues in mind, where the nodes are all
assumed to be honest. In practical deployment scenarios, this assumption does
not hold. This chapter provides a comprehensive overview of security issues in
WMNs and then particularly focuses on secure routing in these networks. First,
it identifies security vulnerabilities in the medium access control (MAC) and
the network layers. Various possibilities of compromising data confidentiality,
data integrity, replay attacks and offline cryptanalysis are also discussed.
Then various types of attacks in the MAC and the network layers are discussed.
After enumerating the various types of attacks on the MAC and the network
layer, the chapter briefly discusses on some of the preventive mechanisms for
these attacks.Comment: 44 pages, 17 figures, 5 table
Efficient quantum key distribution over a collective noise channel
We present two efficient quantum key distribution schemes over two different
collective-noise channels. The accepted hypothesis of collective noise is that
photons travel inside a time window small compared to the variation of noise.
Noiseless subspaces are made up of two Bell states and the spatial degree of
freedom is introduced to form two nonorthogonal bases. Although these protocols
resort to entangled states for encoding the key bit, the receiver is only
required to perform single-particle product measurements and there is no basis
mismatch. Moreover, the detection is passive as the receiver does not switch
his measurements between two conjugate measurement bases to get the key.Comment: 6 pages, 1 figure; the revised version of the paper published in
Phys. Rev. A 78, 022321 (2008). Some negligible errors on the error rates of
eavesdropping check are correcte
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