Working with Sensitive or Confidential Data John Southall Bodleian Data Librarian Subject Consultant for Economics, Sociology, Social Policy and Intervention 2015

Types of Data Numerical Text based Audio Visual Each are representations of information that may be confidential Goal: understand impact of this on research process and manage it Staring point is to think about why your data is considered confidential or sensitive in some way.

Focus on Reasons Why confidential? Is it because of... General content Particular content Undertakings given to research participants Focus on the reasons because each will contain the seeds of the solution

General Content Subject matter as a whole is sensitive or confidential Non-sensitive elements cannot be separated The next question to think about why your data is considered confidential or sensitive in some way.

Particular Content Are there specific sections that are confidential Example; survey data where post codes have been collected as one of the variables Example; Interview data that includes names and accounts of illegal activity Possibility of tagging and removal? The next question to think about why your data is considered confidential or sensitive in some way.

Consent Undertakings Poorly worded participation agreements may over emphasise sensitivity Help ensure participation Avoid agreements that are too restrictive “only to be used by this researcher” “will be destroyed” “No one else will read” Helpful but not too restrictive The next question to think about why your data is considered confidential or sensitive in some way.

Overall Data type is never the real problem e.g. Audio data It’s confidential because of... General or particular content Consent agreements Each will shape how you use data And how it will be preserved and shared Data format – blanket assumptions that data is sensitive because it is a recording per se are wrong. It is still about content that needs to be blurred or restricted.

Sharing thoughts on this Understanding reasons your data is confidential helps you think about how to make best use of it Now - in your current project In the future RDM support network at Oxford and Data Librarian are here to discuss and support

Dealing with the issues At collection stage Usage and access during your project How it will be accessed after the project

Informed Consent Undertakings need to encourage participation Create trust BUT also produce workable data Look at examples Pilot your agreements Negotiate

Elements of Data Storage Securely holding the data is only one part Enabling efficient access Short term Long term Encryption / disaster planning Managing data Version control Honouring agreements made

Blurring, Masking or Anonymisation Light touch; limited key identifiers e.g. Names and addresses only Replacement / Pseudonyms – data blurring Aggregation – fine grain detail/numbers removed Perturbation e.g. Barnardisation Randomised sampling

Blurring, Masking or Anonymisation Perhaps best used for particular content Removing columns from spreadsheets Specific names/words in transcripts But – too blunt a tool? Dangers of data degradation or distortion Is there an alternative?

Restricting Access Anonymisation allows wide access to data by removing content An alternative approach is to leave detail but make access harder E.g. Microdata from Eurostat Vetting of access from UKDS Create clear access and usage conditions Restrict what content may be reproduced Introduce embargoes (last resort)

Restricting Access Best for concerns about general content? Has to be planned from beginning Effective or credible policing of restrictions needed Both approaches can increase usage potential of data but require planning

Typical Restrictions For a specific research project only Cannot be shared or preserved afterwards Non-commercial use Source cited Re-purposing by permission Tabulation or other visualisations Copyright and Theses It is your responsibility to be clear on what restrictions a third party data supplier imposes

Some Tactics to Allow Effective Use Document the research process Metadata that captures decisions and choices Pilot consent paperwork Think about what could go wrong! Collecting inappropriate data Hardware /software failure Theft Confusion over multiple versions Managing accusations of disclosure

Confidentiality (Conclusion) Content is only starting point Actual issue is how information will be used, managed, and disseminated. Maintain confidentiality through controls on handling, storage and sharing Draw on RDM support at Oxford Make decisions that allow research design to reduce risks of disclosure