Operationalising ‘safe statistics’ the case of linear regression Felix Ritchie Bristol Business School, University of the West of England, Bristol

Background: output SDC Safe statistics in principle Making it work: regression and totals What does ‘safe’ mean? Plan

Researchers increasingly using very sensitive data ‘Traditional’ SDC research (tables and anonymisation) of limited relevance Need rules for generalised output  ‘Output SDC’  Ideally, principles-based Background: output SDC

How do you devise guidelines for output when everything is possible?  The ‘research zoo’ –Separate lions from rabbits –Focus on the lions –Forget about the rabbits Making output SDC manageable

Define a statistic (sum, regression, odds ratio, index etc) as ‘safe’ or ‘unsafe’ –safe: release unless there’s a reason –unsafe: don’t release unless shown to be safe in the specific context SDC efforts concentrated on problematic output ‘Safe statistics’

1.Define the functional form 2.Identify the disclosure potential 1.Can it directly reveal a single data point? 2.Can it be differenced? 3.Anything else? 3.If provisionally ‘safe’, identify special cases 4.Draft guidelines Categorising ‘safe statistics’

Example: regression coefficients

Example: total

Safe statistics decision chart

We can’t –nor are we trying to ‘safe’ = ‘for all practical purposes posing no significant disclosure risk’ ‘unsafe’ = ‘high risk; spend time on this’ How can we say X is unconditionally ‘safe’?

Everything has a theoretical risk Resources are limited Overall risk protection is maximised by concentrating on real risks …and also the non-experienced Risk assessment for grown-ups

Questions