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IPS155 Laura B.
for any given variable and stratification variable (Freiman et als., 2016).
Another possible solution has been recently proposed (Chetty and
Friedman, 2019), which consists in adding noise to each statistics in
proportion to its sensitivity to the addition or removal of a single
observation from the data, in order to more efficiently cope with the trade-
off between privacy loss and accuracy.
Another innovation of the Banca d’Italia’s RDC will be the creation of a
data enclave, a laboratory, situated in Banca d’Italia premises, where
external researchers can receive assistance from a dedicated personnel and
perform analysis on reserved datasets, today not available for external
researchers (namely for datasets other than households’ and firms’ survey
data). The creation of a RDC is also part of Banca d’Italia’s commitment as
participant of the International network of exchanging experiences on
statistical handling of granular data (INEXDA), whose final goal is to facilitate
the use by external economists of granular data produced by participating
institutions for research and comparisons. The memorandum of
understanding already signed by the 7 participating institutions foresees
two steps: perform a pilot exercise consisting in a detailed inventory of
available databases and existing procedures; explore harmonization for
future integration of participating RDCs. This will possibly allow in the future
to make comparative analysis using granular data on reserved datasets,
thereby heavily contributing to understand international heterogeneities on
firms’ and households’ behavior.

References
1. Bruno, G., D’Aurizio, L., Tartaglia Polcini, R. (2011). The Bank of Italy’s
experience with remote processing of business microdata. Mimeo.
2. Chetty, R., Friedman, J.N. (2019). A practical method to reduce privacy
loss when disclosing statistics based on small samples. America
Economic Review Papers and Proceedings. Forthcoming.
3. Freiman, M. H., Schar, B., Hasenstab, K., Lauger, A. (2016). Evaluating a
remote access system. Research Report Series. Center for disclosure
avoidance research. US Census Bureau.
4. Lane, J., Schur, C. (2010). Balancing Access to Health Data and Privacy: A
Review of the Issues
and Approaches for the Future. Health Services Research. Vol.45.
5. Schiller, D., Welpton, R. (2013). Providing remote access to European
microdata. Conference paper. NTTS.
6. Schouten, B., Cigrang, M. (2003). Remote access systems for statistical
analysis of microdata. Discussion paper n.03004. Statistics Netherlands

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