Optimal Scope for Free Flow of Non-Personal Data in Europe

15-03-2018

Data is not static in a personal/non-personal classification – with modern analytic methods, certain non-personal data can help to generate personal data – so the distinction may become blurred. Thus, de-anonymisation techniques with advances in artificial intelligence (AI) and manipulation of large datasets will become a major issue. In some new applications, such as smart cities and connected cars, the enormous volumes of data gathered may be used for personal information as well as for non-personal functions, so such data may cross over from the technical and non-personal into the personal domain. A debate is taking place on whether current EU restrictions on confidentiality of personal private information should be relaxed so as to include personal information in free and open data flows. However, it is unlikely that a loosening of such rules will be positive for the growth of open data. Public distrust of open data flows may be exacerbated because of fears of potential commercial misuse of such data, as well of leakages, cyberattacks, and so on. The proposed recommendations are: to promote the use of open data licences to build trust and openness, promote sharing of private enterprises’ data within vertical sectors and across sectors to increase the volume of open data through incentive programmes, support testing for contamination of open data mixed with personal data to ensure open data is scrubbed clean - and so reinforce public confidence, ensure anti-competitive behaviour does not compromise the open data initiative.

Data is not static in a personal/non-personal classification – with modern analytic methods, certain non-personal data can help to generate personal data – so the distinction may become blurred. Thus, de-anonymisation techniques with advances in artificial intelligence (AI) and manipulation of large datasets will become a major issue. In some new applications, such as smart cities and connected cars, the enormous volumes of data gathered may be used for personal information as well as for non-personal functions, so such data may cross over from the technical and non-personal into the personal domain. A debate is taking place on whether current EU restrictions on confidentiality of personal private information should be relaxed so as to include personal information in free and open data flows. However, it is unlikely that a loosening of such rules will be positive for the growth of open data. Public distrust of open data flows may be exacerbated because of fears of potential commercial misuse of such data, as well of leakages, cyberattacks, and so on. The proposed recommendations are: to promote the use of open data licences to build trust and openness, promote sharing of private enterprises’ data within vertical sectors and across sectors to increase the volume of open data through incentive programmes, support testing for contamination of open data mixed with personal data to ensure open data is scrubbed clean - and so reinforce public confidence, ensure anti-competitive behaviour does not compromise the open data initiative.