This book sets out to address some of the issues that a smart city needs to overcome to make use of both the data currently available to them and how this can be enhanced by using emerging technology enabling a citizen to share their personal data, adding value.
It provides answers for those within a smart city, advising their mayors or leaders on introducing new technology. We will cover the topic so as to enable many different public officials to be able to understand the situation from their own perspective, be they lawyers, financial people, service providers, those looking at governance structures, policy makers, etc.
We are contributing to the new model for the European Data Economy. Case studies of existing best practice in the use of data are augmented with examples of embracing a citizen’s personal data in the mix, to enable better services to develop and potential new revenue streams to occur. This will enable new business models and investment opportunities to emerge.
We will address the topic of how to put a value on data and will conclude by looking at what new technologies will be emerging in the coming years, to help cities with carbon-neutral targets to have more chance of succeeding.
Author(s): Shaun Topham, Paolo Boscolo, Michael Mulquin
Series: River Publishers Series in Energy Sustainability and Efficiency
Publisher: River Publishers
Year: 2023
Language: English
Pages: 357
City: Gistrup
Front Cover
HalfTitle
RIVER PUBLISHERS SERIES IN
ENERGY SUSTAINABILITY AND EFFICIENCY
Title
Copyrights
Contents
Preface
Acknowledgements
List of Contributors
List of Figures
List of Tables
List of Abbreviations
Introduction
1 Peril on the Road to Utopia – Opportunities and Risks of Infusing Personal Data into the Smart City Ecosystem
1.1 Introduction
1.2 Broken Promises
1.2.1 The smart city is finally coming of age
1.2.2 Is the internet broken?
1.3 Promising Responses
1.3.1 European legislation
1.3.2 Taking back control with data vaults
1.4 Think!
1.5 Personal Data Vaults Matter
1.5.1 Capturing and influencing the citizen journey
1.5.2 Who will help us?
1.5.3 Personal benefits of the PDV
1.6 Utopia or Dystopia? A Scenario Analysis
1.6.1 Scenario drivers
1.6.2 Four scenarios
1.6.3 Strategies to reach Utopia
1.7 Personal Data: “Fragile, Handle with Care”
2 The Principal Projects Underpinning This Work
2.1 Project Overviews
2.2 DataVaults
2.3 KRAKEN
2.4 Safe-DEED
2.5 DUET Project
2.6 InteropEHRate
2.7 RUGGEDISED
2.8 DataPorts
2.9 EUHubs4Data
2.10 i3-MARKET
2.11 AURORAL
2.12 REPLICATE
2.13 PIMCity
2.14 smashHIT
2.15 PolicyCloud
2.16 IRIS: Co-creating Smart and Sustainable Cities
2.17 SmartEnCity
2.18 The MyData Global Initiative
2.19 The SOLID Initiative
3 Best Practice in the General Use ofData in a City
3.1 Flanders, Belgium
3.2 Pilsen, Czech Republic
3.3 Camden, London, United Kingdom
3.4 Trikala, Greece
3.5 Umeå, Sweden
3.6 Tampere, Finland
3.7 Cities with Universities: KRAKEN and Students
3.8 Rotterdam, Netherlands
3.9 Athens, Greece
3.10 City Health Organisations and the KRAKENHealth Application
3.11 Sofia, Bulgaria
3.12 Piraeus, Greece
3.13 Grand Lyon (Metropolis of Lyon), France
3.14 Prato, Italy
3.15 Eilat, Israel
3.16 Florence, Italy
3.17 SmartEnCity: Vitoria-Gasteiz, Spain
3.18 SmartEnCity: Tartu, Estonia
3.19 Helsinki, Finland
3.20 Glasgow, Scotland
4 Case Studies Involving the Use ofPersonal Data in a Smart City
4.1 MIWenergia in the DataVaults Project
4.2 Prato’s Usage of a Citizen’s Personal Data
4.3 Piraeus’s Use of Personal Data
4.4 Olimpiacos: Interaction with the Fan-Base
4.5 Olimpiacos: Athletes Sports and Activity Data Sharing
4.6 Andaman7 Health Application
4.7 Smart City Graz
5 The Local Data Economy
5.1 Introduction
5.2 i3-MARKET
5.3 AURORAL
5.4 The smashHIT Project
5.5 The smashHIT Methodology
5.6 Conclusion
6 Technical Components
6.1 Introduction
6.2 Data Owners and Subjects Controlling their Own Data
6.2.1 User personas
6.2.2 Direct anonymous attestation (DAA)
6.2.3 Access control policies
6.2.4 Data owners consent management
6.3 Preserving Data Privacy andData Quality Simultaneously
6.3.1 Data anonymisation
6.3.2 Secure data analytic services
6.3.3 Data management technologies
6.3.4 Data models and interoperability
6.3.5 Digital twins for privacy preservation
6.3.6 Cryptographic solutions for data privacy
6.3.7 Artificial intelligence threat reporting andresponse systems
6.4 Information Delivery on Privacy Metrics andData Content and Value
6.4.1 Privacy metrics and risk management andprivacy metrics for personal data
6.4.2 Personal data analytics
6.4.3 Data valuation
6.5 Data Platforms
6.5.1 Secure and trusted data communication channels
6.5.2 Immutable ledgers and smart contracts
6.5.3 Crypto wallets
6.6 Other Supporting Initiatives
6.6.1 EUHUBS4DATA
6.6.2 MyData
6.6.3 Solid Flanders
6.6.4 Big value data association (BDVA)
6.7 Looking into the Future
7 Interoperability and the Minimal Interoperability Mechanisms
7.1 The Context – The Local Data Sharing Ecosystem
7.2 Interoperability
7.3 The European Policy Context
7.4 Minimal Interoperability Mechanisms
7.5 The Individual MIMs
7.5.1 MIM1 context information management
7.5.2 MIM2 shared data models
7.5.3 MIM3 finding and using the data
7.5.4 MIM4 personal data management
7.5.5 MIM5 fair and transparent AI
7.5.6 MIM7 geospatial information management
7.6 MIMs Plus
8 Health Data in a Smart City
8.1 Is Health Data Important for a Smart City?
8.2 The Conflict of Interest
8.3 Maybe Anonymisation is a Solution?
8.4 Health of Citizens and Health of the City
8.5 Health Data Interoperability
8.5.1 Why is it hard?
8.5.2 Unstructured data
8.5.3 Structured data
8.5.4 Is the situation different in the USA?
8.6 The InteropEHRate Project
8.7 Data Ownership and the Distributed Approach
9 Personal Data Management and MIM4
9.1 The Fragmented Marketplace
9.2 MIM4
9.2.1 Capabilities
9.2.2 Requirements
9.3 The Link with National ID/Citizen Cards
10 Standards for Citizens
10.1 Introduction
10.2 The Background
10.3 Citizen Standards in Smart Communities
10.4 Looking Ahead
11 Business Models
11.1 Introduction
11.2 Business Models and Smart Cities
11.3 Smart City Networks Creating Best Practice Repositories
11.4 SmartEnCity Project
11.5 Urban Data Platforms
11.6 REPLICATE Project
11.7 IRIS Project
11.8 IRIS Study and the Smart City BusinessModel Canvas (SC-BMC)
11.9 REPLICATE Project
11.10 RUGGEDISED Project
11.11 Safe-DEED
11.12 The Safe-DEED Tools
11.13 DUET Project
11.14 DataVaults Project
11.15 Viewpoint from a DataVaults SME’s Perspective
11.15.1 Assentian
11.15.2 Andaman7
11.16 Digital Twins and Business Models
11.17 Conclusion
12 (Digital) City Financing Platforms
12.1 Introduction
12.2 Role of Financing Platforms
12.3 But Who are These Digital Financing Platforms –Or Where are They?
12.3.1 Examples of digital financing platforms
12.3.2 Credit/loans
12.3.3 Re-financing
12.3.4 Challenge project pipeline: the chicken and egg problem
12.4 Conclusion
13 The Governance of Personal Data for the Public Interest: Research Insights and Recommendations
13.1 Introduction
13.2 Alternative Models for Data Governance
13.3 City Administrations’ Access to Personal Data of Public Interest
13.4 A Few Recommendations for Cities
14 Data Valuation and Its Applications for Smart Cities
14.1 Introduction
14.2 Defining the Value of Data
14.2.1 Data through an economic lens – trading data
14.2.2 The price of personal data – a chaotic landscape
14.2.3 Challenges defining the value of data –beyond financial value
14.3 The Data Valuation Process
14.3.1 Data contexts
14.3.2 Data quality assessment
14.3.3 Data quality metrics and dimensions
14.4 Aggregating and Reporting the Value of Data
14.5 Takeaways for Cities
15 Does Everything Conform to Legal, Ethical, and Data Protection Principles?
15.1 Introduction
15.2 The Evolving Regulatory Framework Relevant to the Personal Data Sharing Platforms
15.3 Existing Regulatory Framework
15.4 The Regulatory Reforms Under Development
15.5 Main Legal and Ethical Challenges and Technology-enabled Opportunities to Tackle with Them
15.6 The Need to Avoid Consent Fatigue and to Develop and Use User- and Data-Protection-Friendly User Interface
15.7 Risk-based Approach and Risk-Exposure Dashboard
15.8 Personas and Digital Twins
15.9 Challenges Related to Smart Contracts, the eIDAS Regulation, and the Self-Sovereign Identity
15.10 DataVaults as a Flagship Initiative for Personal Data Sharing Under User Control and Benefitting All the Actors Involved: Experiences and Lessons Learnt
15.11 Case Study: Approach and Legal and Ethical Requirements for DataVaults Ethical Policy
15.11.1 Ethics and data protection impact assessment methodology
15.12 Conclusion
16 Data-Driven and Citizens’ Inclusive Smart Cities: Top-Down and Bottom-Up Approaches to Tackle Societal and Climate Challenges
16.1 Introduction
16.2 Sharing and Networking on Citizen Engagement in Europe. Resources and Lessons Learnt from the Citizen Focus Action Cluster of the Smart Cities Marketplace
16.3 Good Practices. Citizen Generated Data to Improve Urban Innovation and Smart Cities Policies.Top-Down and Bottom-Up Approaches
16.3.1 Harnessing open data for evidence-based urbanpolicies – the Camden and Sofia use-cases
16.3.2 Crowdsourced data for enhancing safety perception in public space and transport
16.4 Envisioning the Future of Citizens’ Intelligent Cities and the Role of Citizen Engagement
17 What Next?
17.1 Moving Towards a European Model for the Data Economy
17.2 The Focus for Follow-up Activity
17.3 The Story of Data
17.4 Business Models
17.5 A “Personal Data-Smart Cities” Group
17.6 Citizen Engagement
17.7 Governance
17.8 Interoperability
17.9 Legality
17.10 On the Horizon
17.11 Contracts to Have Data Plan
17.12 Concluding Remark
Index
About the Contributors
About the Editors
Back Cover
