About
Find Out More About PROTECT
Protecting Personal Data Amidst Big Data Innovation
PROTECT is an European Training Network (ETN) funded under the EU’s Marie Skłodowska-Curie Actions.
PROTECT researchers are receiving a strongly multidisciplinary training programme in Data Protection Law, Technology Ethics and Knowledge Engineering, spread throughout 3 different Work Packages:
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WP1 - Privacy Paradigm
Develop and assess standard forms for privacy policies consistent with GDPR requirements
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WP2 - Ethics of Personalisation
Assess and refine existing ethics' assessment tools for emerging technologies
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WP3 - Processing and Purpose in Personal Data Management
Expand existing techniques to support the development of legal and technical risk assessments
Webinar
Check out our Webinar
A discussion on rebalancing control between big tech and the citizen and how proposed new EU legislation can support new trust models.
Results
Summary of the main findings of the PROTECT Think-In Events
Common topics revolve around the enforcement of individual control over data through technological developments such as personal data stores and trust on public entities over private ones for the management of said data.
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Law Enforcement
Challenges of EU/non-EU personal data transfers due to technological developments
Will more regulation address this?
Current form of terms & conditions/privacy notices is meaningless, and language is too complex
Avoid turning protecting regulation, such as the GDPR, into a checkbox compliance exercise
Existence of guiding/recommending bodies that can point out to more “trustworthy” entities
Involvement of users in the process of creating the privacy notice
International bodies to go beyond the limits of GDPR
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Address Failures in Trust
Emergence of new data trust schemes
Ensure explainability to users
Data subjects trust public entities more than private entities
Government entities should foster trust
Collaboration between public and private sectors towards fostering trust
Public bodies should have control over and oversight data flows
The reputation of big vs small companies - Create a standard for privacy rating of companies
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Educational / Social Concerns
Educate citizens in privacy and data protection
Different social classes and different attitudes towards controlling data
Being indifferent to data control due to lack of awareness of the negative consequences of losing control over one's data
Relevance of “personal” (in particular, family) relations to foster trust in online services
The reverse of education (from children to parents) about data literacy
Govern the data of vulnerable data subjects (elderly, mental illness, children, ...)
Different degrees of trust in social relationships and institution-based relationships
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Personal Data Sharing Conditions
The type of data conditions and the desired level of control over it
Individual (enforceable) control over data through technological developments (personal data stores) could play a great part
Balance time spent on data managing and convenience of not worrying about it
Concern about sensitive data types such as health or financial data
Possibility to share personal data only for certain purposes or to third parties
Relevance of consent as an enabler of data sharing - Use of dynamic consent and technological developments for it
Return on investment of providing personal data
Sharing data only for the people who are not able to make decisions