State Statistical Institute Berlin-Brandenburg Jörg Höhne / Julia HöningerResearch Data Centre Morpheus – Remote Data Access with a Quality Measure Joint.

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State Statistical Institute Berlin-Brandenburg Jörg Höhne / Julia HöningerResearch Data Centre Morpheus – Remote Data Access with a Quality Measure Joint UNECE /Eurostat Work Session on Statistical Data Confidentiality 26 – 28 October 2011

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 2 Need for automatic output checking Current situation: remote processing Researchers want results fast Manual control time-consuming  In need for a system that can check results automatically  On the way to real remote access  Work part of the project „infinitE - an informational infrastructure for the E-Science Age“  funded by German Federal Ministry of Education and Research

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 3 Morpheus – general idea Users run calculations on anonymous data Additional to anonymous results a measure of quality Results (almost) in real time For final publication: Results on original data, checked manually

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 4 Morpheus – functionality Distance of anonymous to original result

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 5 Advantages for users Compared to current remote processing: Fast return of results Browsing in data set allowed (almost) all analysis permitted Compared to scientific use files: Measure of quality for all results Challenge: Reading output

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 6 Challenges for RDC staff Generate anonymous files for all data sets  Census data  Business data  Panel data Perturbative anonymisation methods or synthetic data  Reduction of manual work load for RDC staff if the anonymous data set is good enough

Höhne / Höninger State Statistical Institute Berlin-Brandenburg Morpheus – a first prototype First case study with Morpheus Statistical software Stata Anonymous file for business statistics Program from an actual user Works for output from SPSS and SAS. tabstat expshare2000, stats(N mean sd p25 p50 p75) variable | N mean sd p25 p50 p expshare2000 |

Höhne / Höninger State Statistical Institute Berlin-Brandenburg Morpheus - regression output. xtreg lnapro export pers perssq hc, fe r | Robust lnapro | Coef. Std. Err. t P>|t| [95% Conf export | pers | e E perssq | 1.37e e e E E E E-12 hc | _cons |

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 9 Disclosure risk of absolute deviation Risks can arise if direction of deviation is clear because of logical reasons: Variable must be non-negative, but distance is bigger than absolute value  Example: average number of employees = 1000  Adding stochastic noise to the quality 1500

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 10 Variants of modifying the distance stochastically result with anonymous data true result a) Unbiased point estimate b) Maximum distance

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 11 Remaining questions Remaining disclosure risks How to deal with distance 0? Testing acceptance among users  „use Morpheus or wait“ Independent of anonymisation technique IT server environment

Höhne / Höninger State Statistical Institute Berlin-Brandenburg 12 Thank you for your attention! Contact Dr. Jörg Höhne Julia Höninger