Page 475 - Invited Paper Session (IPS) - Volume 1

IPS177 F. Ricciato et al.
Some initial work in this direction is starting to appear in the field of Official
Statistics [9], while commercial implementation of SMC already include some
simple safeguards for disclosure control [8].
In conclusion, the new datafied scenario requires SOs to widen their
traditional approach to privacy and data confidentiality. Purely regulatory
means in the back-end and simple SDC methods in the front-end might not
suffice any more. Embracing novel tools such as SMC, in combination with
more advanced forms of dynamic SDC, seems to be a promising direction to
move forward. More in general, SO need to develop a more systematic and
articulated approach towards confidentiality engineering to face the new
challenges posed by an increasingly complex data ecosystem.

References
1. K. Cukier and V. Mayer-Schoenberger. The rise of big data. Foreign
Affairs, May/June 2013. URL
https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data.
2. J. M. Abowd and I. M. Schmutte. An economic analysis of privacy
protection and statistical accuracy as social choices. American
Economic Review (forthcoming), August 2018.
https://arxiv.org/abs/1808.06303.
3. J. Domingo-Ferrer, S. Ricci, and J. Soria-Comas. A methodology to
compare anonymization methods regarding their risk-utility trade-
off. Int. Conf. on Modeling Decisions for Artificial Intelligence
(MDAI 2017), August 2017.
4. F. Ricciato et al. Towards a Reference Architecture for Trusted Smart
Statistics. 104th DGINS conference, Bucharest, Romania, 10-11
October 2018. https://tinyurl.com/yco77y62.
5. T. Wang and L. Liu. Output privacy in data mining. ACM
Transactions on Database Systems, 36(1), March 2011. DOI:
0.1145/1929934.1929935.
6. R. Cramer, I. Damgard, and J. Buus Nielsen. Secure Multiparty
Computation and Secret Sharing. Cambridge University Press, 2015.
7. S. Anspal, M. Kaska, and I. Seppo. Using k-anonymization for
registry data: pitfalls and alternatives. IEEE Trans. on
Dependable and Secure Computing, 2017.
http://dx.doi.org/10.12697/ACUTM.2017.21.05.
8. D. Bogdanov et al. Rmind: a tool for cryptographically secure statistical
analysis. IEEE Trans. on Dependable and Secure Computing, 15(3),
May/June 2018.
9. K. Shirakawa et al. A proposal of a simple and secure statistical
processing system using secret sharing. UNECE Work Session on
Statistical Data Confidentiality, Skopje, 20-22 Sept. 2017.

464 | I S I W S C 2 0 1 9