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Privacy Business

Privacy Business

How will privacy issues affect Internet business models

Privacy protection and use of personal data are central issues in how the Internet is being used. In an economy where data is the 'new oil', personal data is a highly valuable resource.

This study summarizes the issues related to the concept of privacy and personal data protection. It addresses the question of putting a value on data. It describes recent regulatory developments in the field.

We analyse the technology behind digital trust platforms before presenting an overview of the solutions being used and considered for implementing services. Then we illustrate the latest trends for new services on the market.

A SWOT analysis for GAFA and other businesses is then provided before proposing three scenarios for the service approach businesses may take.


1. Executive Summary
2. Methodology & definitions
2.1. General methodology of IDATE's reports
2.2. Methodology specific to this report
2.3. Definitions
3. Issues and key factors
3.1. Drivers of the privacy business
3.2. Protection of privacy and trust in the digital world
3.2.1. Trust, a fuzzy but essential data component
3.2.2. How are trust relationships in the 2.0 world?
3.3. The trust ecosystem (circle of trust)
3.3.1. Trust is a process
3.3.2. Roles in the trust chain
3.3.3. Trust relationships between players
3.4. Important role played by the current regulatory environment
3.4.1. Impact of the regulatory framework on market structure
3.4.2. Focus on European personal data protection regulations
3.4.3. European electronic identification regulations: eIDAS
3.4.4. United States
4. Characteristics of the privacy and personal data economy
4.1. What value can be placed on personal data and privacy?
4.1.1. Approaches to putting a price on personal data
4.1.2. Creating value from protecting privacy
4.1.3. Consumer push-back and economic inefficiency
4.1.4. The clear loss of trust in the system is leading to a paradoxical situation
4.2. How marketing concepts and strategies are evolving
5. Technologies and services
5.1. De-identifying user data
5.2. Identity tools looking for a good 'form-factor'
5.3. Behavioural analysis for identification purposes
5.4. Platforms and identity federation
5.5. The personal cloud
5.5.1. Personal Data Stores (PDS)
5.5.2. Privacy Trust Frameworks
5.5.3. The promise of the Life Management Platforms (LMP)
5.6. Looking towards a global vision
6. The offering: typology of privacy management services
6.1. Helping users protect and manage their personal data
6.2. Helping users manage their daily lives
6.3. Helping users to act, be better informed, plan and make the right decisions
7. Brand strategies and market-makers
7.1. GAFA strategies for personal data
7.1.1. Overview: GAFA's strategic positioning in relation to market developments and the personal data protection issue
7.1.2. Two models up against each other on the ground
7.1.3. An intermediate model: Microsoft
7.2. Business strategies (B2C)
7.2.1. Overview: strategic positioning (B2C) in relation to market developments and the personal data protection issue
7.2.2. Example scenarios
7.2.3. Focus on banks and insurance companies
7.2.4. Focus on telecom operators
List of Tables and Figures
Tables
Table 1: Basic dimensions to trust with regard to personal data
Table 2: Key principles for protecting personal data in France
Table 3: Use cases for collecting attributes in real time
Table 4: French regulatory principles
Table 5: What will change in the European regulations on privacy protection
Table 6: Major FTC regulations by area
Table 7: FTC's sector regulation
Table 8: Approaches to personal data valuation put forward by the OECD
Table 9: Changes in behaviours are changing the game
Table 10: Overview of biometric contact points
Table 11: Software contact point examples
Table 12: What's involved with behavioural analysis
Table 13: Examples of public identity federation initiatives
Table 14: Main benefits of Customer Identity Management Platforms
Table 15: Main features of Customer Identity Management Platforms
Table 16: Privacy technology platform classification
Table 17: Examples of PDS players
Table 18: GAFA's fundamental strategies
Table 19: SWOT analysis of GAFA's positioning in relation to market developments and the personal data protection issue
Table 20: SWOT analysis of strategic positioning (B2C) in relation to market developments and the personal data protection issue
Table 21: Possible action scenarios for businesses 65
Figures
Figure 1: Breakdown of time spent on the Internet, January 2015
Figure 2: Confidence levels of Americans towards their service providers
Figure 3: Security concerns in the cloud's infrastructure layers, according to Amazon Web Services
Figure 4: Digital trust ecosystem
Figure 5: Reputation attributes of BlaBlaCar
Figure 6: Security market share in the Alexa top 1M websites
Figure 7: Overview of trust environments
Figure 8: Example of a commercial e-Signature trust service
Figure 9: Trade-offs weighed by users in exchange for their personal data
Figure 10: Subjective valuation of personal data
Figure 11: GlobalWebindex survey
Figure 12: Mapping different marketing strategies
Figure 13: Intent marketing according to Gigya
Figure 14: The power of 'micro-moments' according to Google
Figure 15: Keys to a winning relationship marketing culture
Figure 16: Positioning of Vendor Relationship Management
Figure 17: Overview of authentication methods
Figure 18: Example of a multi-method enterprise platform
Figure 19: Social login market shares as of 15/11/2015
Figure 20: A Customer Identity Management Platform
Figure 21: The Personal Data Store concept
Figure 22: PDS examples (Personal, Meeco)
Figure 23: Implementation of Privacy-by-Design principles by Safe-BioPharma
Figure 24: Mydex (United Kingdom)
Figure 25: Illustration of a Life Management Platform
Figure 26: Key principles of controlled information sharing
Figure 27: A Life Management Platform governance framework
Figure 28: MyPermissions
Figure 29: Blue Button apps by Humetrix
Figure 30: digi.me
Figure 31: Comparison of the personal assistants of GAFA
Figure 32: EasilyDo
Figure 33: Gocompare
Figure 34: CTRLio
Figure 35: PatientsLikeMe
Figure 36: OptimizeMe
Figure 37: Apple's model vs the Google/Amazon/Facebook model
Figure 38: Microsoft's big picture
Figure 39: Microsoft's HeathVault
Figure 40: Spout
Figure 41: Mobile Connect
Figure 42: ARPU contribution related to identity for MNOs

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