scholarly journals A Transparent and Privacy-preserving Healthcare Platform with Novel Smart Contract for Smart Cities

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Abdullah Al Omar ◽  
Abu Kaisar Jamil ◽  
Amith Khandakar ◽  
Abdur Razzak Uzzal ◽  
Rabeya Bosri ◽  
...  
Keyword(s):  
Smart Cities ◽  
Privacy Preserving ◽  
Smart Contract

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

Cyber Security Challenges for Smart Cities

2022 ◽  
pp. 1459-1480
Author(s):  
Anand Nayyar ◽  
Rachna Jain ◽  
Bandana Mahapatra ◽  
Anubhav Singh
Keyword(s):  
Cyber Security ◽  
Smart City ◽  
Smart Grids ◽  
Data Transfer ◽  
Smart Cities ◽  
Privacy Preserving ◽  
Automation System ◽  
Security Models ◽  
The People ◽  
Aerial Vehicle

Smart cities are composed of interlinked components with constant data transfer and services targeted at increasing the life style of the people. The chapter focuses on diverged smart city components as well as the security models designed to be implemented. The four major paradigms discussed in this chapter are smart grids, building automation system (BAS), unmanned aerial vehicle (UAV), and smart vehicles. Apart from addressing the security concerns of every component, the major highlights of this chapter are architecture, smart environment, industry, lifestyle, services, and digital lifestyle quality. Finally, the chapter focuses on privacy preserving mechanisms, its essence over smart cities, strong architecture related to privacy, preserving mechanism, and various approaches available that can retaliate these issues in a smart city environment.

scholarly journals SABlockFL: a blockchain-based smart agent system architecture and its application in federated learning

2020 ◽  
Vol 4 (2) ◽  
pp. 133-147
Author(s):  
Zhizhao Zhang ◽  
Tianzhi Yang ◽  
Yuan Liu
Keyword(s):  
Neural Network ◽  
System Architecture ◽  
Data Exchange ◽  
Privacy Preserving ◽  
Agent System ◽  
Content Type ◽  
New Era ◽  
Agent Model ◽  
Blockchain Technology ◽  
Smart Contract

Purpose The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques. Design/methodology/approach In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage. Findings A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design. Originality/value The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

scholarly journals Blockchain Technology and It’s Applications in E-Governance Services

2019 ◽  
Vol 8 (4) ◽  
pp. 5795-5802
Keyword(s):  
Supply Chain ◽  
Data Structure ◽  
Open Source ◽  
Smart Cities ◽  
Third Party ◽  
Technical Aspects ◽  
Blockchain Technology ◽  
Smart Contract ◽  
Peer Network ◽  
Peer To Peer Network

Blockchain Technology is one of the most popular technologies of present days. This technology has the capability to eliminate the requirement of third party to validate the transactions over the Peer-to-Peer network. Due to various features of Blockchain like smart contract, consensus mechanism, network transactions are completed securely, efficiently and timely. This technology is very useful in many areas including medical, IoT, e-Governance services, smart cities, taxation, supply chain, banking etc. In this paper, we discuss the Blockchain Technology in detail, its data structure, open source platform like Ethereum and Hyperledger, technical aspects of this technology, possible applications of this technology, challenges and limitations in adaptation of this technology.

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