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BDVe Webinar Series - Why are privacy-preserving technologies not used more widely?

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What approaches are being taken to tackle the policy challenges within the big data landscape, and how are these solutions coping in reality? This webinar will address these issues through the perspective of two projects: e-SIDES and SMOOTH. Daniel Bachlechner, of e-SIDES, will discuss the organizational and technical challenges that privacy-preserving big data technologies present, and how an increased level of dialogue between stakeholders can pave the way for appropriate and fair solutions. Rosa M. Araujo Rivero will delve into the main challenges experienced by SMEs and startups in dealing with GDPR compliance. Rosa’s work with the SMOOTH project will demonstrate how the proposed solutions are experienced in practice.

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BDVe Webinar Series - Why are privacy-preserving technologies not used more widely?

  1. 1. Why are privacy-preserving technologies not used more widely? Daniel Bachlechner, Fraunhofer BDVe Webinar 31 January 2020 Source:https://www.ethicalsocietymr.org/upcoming-events.html
  2. 2. Improve the dialogue between stakeholders and increase the confidence of citizens in data technologies and use e-Sides Ethical and Societal Implications of Data Sciences 2 Objectives and methods ▪ Investigation of related projects through joint workshops, interviews and website analyses ▪ Collection of insight from renowned experts with different backgrounds through workshops and interviews ▪ Review of more than 200 articles including academic papers and study reports ▪ Interaction with a diverse set of stakeholders by means of a collaborative platform Key objectives Main methods Reach a common vision for an ethically sound approach to data use and facilitate responsible research and innovation
  3. 3. e-Sides Ethical and Societal Implications of Data Sciences 3 Phases and focus 36-months project Assess existing technologies Identify implementation barriers Make recom- mendations Assess solutions under development Identify design requirements Identify ethical and societal issues Identify existing technologies Identify existing technologies Conduct a gap analysis Conduct a gap analysis Assess existing technologies
  4. 4. 4 Existing technologies Anonymisation Encryption Accountability Deletion Policy enforcement MPC Sanitisation Transparency Access control User control Access & portability Data provenance Resources: D3.1, white paper Technology classes Anonymisation Encryption Accountability Deletion Policy enforcement MPC Sanitisation Transparency Access control User control Access & portability Data provenance
  5. 5. ▪ Limited integration into today’s big data solutions ▪ Low demand for privacy- preserving big data solutions ▪ Considerable regional differences regarding perception and use ▪ Combination with non-technical measures needed ▪ Unclear responsibilities for protecting privacy 5 Existing technologies Resources: D3.2, white paper, WISP publication Effectiveness and challenges Perception and use ▪ The set of technology classes is comprehensive ▪ Classes of technologies need to be combined to be effective ▪ Technologies pursue different aims ▪ A multidimensional measure is required ▪ There is tension between objectives ▪ Limited integration into today’s big data solutions ▪ Low demand for privacy- preserving big data solutions
  6. 6. Relevant societal and economic aspects 6 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper Costs and benefits ▪ Preserving privacy leads to additional costs but there is only little information about the amount of costs ▪ User inconvenience, for example, has been described as a relevant cost factor ▪ There is no evidence that privacy-preserving solutions lead to increased sales or justify higher prices ▪ The use of privacy-preserving technologies must make economic sense
  7. 7. Relevant societal and economic aspects 7 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper ▪ Privacy preservation may be in conflict with business models ▪ Profits made are often not shared through innovative business models ▪ Trade-off between privacy protection and the utility of data ▪ Fear of limitations in flexibility and the ability to innovate Business models
  8. 8. Relevant societal and economic aspects 8 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper Public attention ▪ Privacy protection is not yet a standard business practice ▪ Actors tend to take extreme positions regarding privacy preservation ▪ Potential to allow for competitive differentiation (e.g., Apple) ▪ Limited transparency with respect to algorithms and data provenance
  9. 9. Relevant societal and economic aspects 9 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper Economic value ▪ No shortage of economic literature attempting to quantify the value of data ▪ Privacy concerns and expectations are context- dependent and difficult to predict ▪ Privacy-unfriendly companies tend to obtain the greater market share ▪ The value of privacy seems to depend on the social class to which an individual belongs
  10. 10. Relevant societal and economic aspects 10 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper Cultural fit ▪ Privacy preferences and practices vary among nations and regions ▪ Broad spectrum of views regarding impacts: from deindividualization to personalization ▪ Less involvement in data management tasks preferred ▪ Extent to which unauthorized secondary use raises concerns differs
  11. 11. Relevant societal and economic aspects 11 Limited integration Costs and benefits Business models Public attention Economic value Cultural fit Skill level Resources: D4.1, white paper Skill level ▪ Often critical people do not know what questions to ask ▪ The data becomes more and more important a new mind-set is required ▪ Integrating and using privacy-preserving technologies requires specific skills
  12. 12. Source:https://leadg2.thecenterforsalesstrategy.com/book 12 Embed security and privacy features Connect people, processes and technology Take preventive measures Comply with laws and corporate policies Resources: D4.2 Connect people, processes and technology Design requirements for data-driven solutions Embed security and privacy features Take preventive measures Comply with laws and corporate policies Going forward
  13. 13. Source:https://www.marblespr.co.uk/news/walk-will-give-next-big-idea-legs-needs/attachment/... 13 Going forward Developers and operators of data-driven solutions Policy makers dealing with relevant issues Developers of privacy-preserving technologies Civil society (organisations) Resources: D5.2 Policy makers dealing with relevant issues Making responsible data driven solutions a reality Developers and operators of data-driven solutions Developers of privacy-preserving technologies Civil society (organisations)
  14. 14. Thank you! @eSIDES_EU #privacyinbigdata eSIDES_EU info@e-sides.eu https://e-sides.eu/

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