1. Harmonisation process of anonymisation of microdata

2. Introduction
Guidelines for anonymisation of social survey microdata have been developed by means of Eurostat methodological framework contract and supplemented with comments and feedback of Expert Group's members.
A framework for harmonised dissemination of microdata datasets has been developed and tested within the ESSnet on SDC.

3. A similar process
Step1
Identify users' needs and constraints of data producer
Step2
Understand data
Step 3
Identify circumstances disclosure is likely to occur (disclosure scenarios)
Step4
Assess disclosure risk and its impact
Step 5
Select appropriate SDC methods
Step 6
Implement and disseminate

4. Applicability Guidelines ESSnet on SDC
– to social surveys + applicable to each country one by one + more flexibility: different approaches possible by country or group of country – more work to reach the best balance between info loss and data protection
+ to microdata (both business and social surveys) + an integrated and harmonised approach - requires a coordinated development of software tool

5. Risk assessment Guidelines ESSnet on SDC
+ does not depend to the purpose of data analysis – subjective concept of data utility – difficult to identify the right balance between data utility and data confidentiality
+ less subjective concepts of benchmarking statistics and setting up thresholds
– highly related to the given purpose of data analysis
the ability of SDC methods to maintain some predefined characteristics is a crucial point

6. SDC methods Guidelines ESSnet on SDC
+ simplicity + flexibility to apply different methods across countries – less harmonisation
– complexity + conditions to apply complex SDC methods are more likely to be met and exploited

7. Governance Guidelines ESSnet on SDC
+ no agreement across countries required
– the governance of a complex production process is one of key issues to be defined and agreed by all the involved actors

8. Conclusion: both approaches can coexists
No comparability of performance can be done as it highly depends on assumptions (disclosure scenarios) and subjective quality measures (data utility versus benchmarking statistics)
Disclosure scenarios are always according to national situations and legislation
Testing phase is crucial in both approaches
Features of statistical domain or statistical analysis are crucial factors in choosing SDC methods
Freedom to data producer which alternative follows