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Archiving Sensitive Data


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June 29th presentation at the session "Extending DSpace" of the Open Repositories conference 2017.

The presentation covers the Metadata Based Access control feature, publicly available in the following codebase:

The talk gives general insights in how the probability and impact can be assessed on two examples of risk: unauthorized access and losing all your data.

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Archiving Sensitive Data

  1. 1. Bram Luyten Tom Desair Open Repositories 2017 Archiving Sensitive Data
  2. 2. Unfortunately Not all repository content is equally open
  3. 3. Overview Metadata based access control Strategies for dealing with sensitive data Actionable takeaways
  4. 4. Question How many authorization groups are there in your DSpace?
  5. 5. Metadata based access control Using EPerson characteristics and Item characteristics to determine whether the EPerson is entitled to access the item. Example: An exact match between a social security number or an email address on the EPerson and on the metadata of the item.
  6. 6. Advantages Scale No identified limits on number of EPeople, items or groups Performance No identified limits on search or item access volumes Can be managed outside of DSpace Both EPerson and Item metadata can be sourced externally Configurable
  7. 7. Configuration example <group-policy groupName="Autenticated_eID_Users"> <exact-match-policy> <itemField>dc.contributor.socialsecurity</itemField> <epersonField>eperson.acl.socialsecurity</epersonField>
 <epersonValueExtractor></epersonValueExtractor> </exact-match-policy> </group-policy>
  8. 8. Disadvantages Edit metadata = Edit authorizations Be very careful of who or what has rights to edit metadata Your metadata becomes even more sensitive The impact of unauthorized access to item metadata may become more severe
  9. 9. Dealing with sensitive data Strategies
  10. 10. Severity is driven by probability and impact
  11. 11. Example 1: Unauthorized access Impact 
 High if you're dealing with sensitive data Low if you're dealing with public/non-sensitive data
 The harder it is for people to access your system, the lower The longer you wait with security updates, the higher
  12. 12. Example 2: Losing all your data Impact 
 High if you're dealing with data that only exists in one place Low(er) if data exists in multiple places
 What does "losing" mean? What does "all" mean?
  13. 13. Actionable takeaways Code available on Feel free to (re)use what you want Assess the severity of your risks by thinking about the associated probability and impact.

  14. 14. Credits Images Keys Tsunami Pick it