Personal Data-Smart Cities: How cities can Utilise their Citizen’s Personal Data to Help them Become Climate Neutral

Front Cover
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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
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