Abstract
Linking data from different sources can enrich the research opportunities in the Social Sciences. However, datasets can typically only be linked if the respondents consent to the linkage. Strategies from the secure multi-party computation literature, which do not require linkage on the record level, might be a viable alternative in this context to avoid biases due to selective non-consent. In this paper, we evaluate whether such a strategy could actually be successfully applied in practice by replicating a study based on linked data available at the Institute for Employment Research. We find that almost identical results could be obtained without the requirement to link the data. However, we also identify several problems suggesting that the proposed strategy might not be feasible in many practical contexts without further modification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beaton, A.E.: The use of special matrix operators in statistical calculus. ETS Res. Bull. Ser 1964(2), i–222 (1964)
Blau, F.D., Kahn, L.M.: The gender wage gap: extent, trends, and explanations. J. Econ. Lit. 55(3), 789–865 (2017)
Blinder, A.S.: Wage discrimination: reduced form and structural estimates. J. Hum. Resour. 8(4), 436–455 (1973). http://www.jstor.org/stable/144855
Blossfeld, H.P., Von Maurice, J.: Education as a lifelong process. Zeitschrift für Erziehungswissenschaft Sonderheft 14, 19–34 (2011)
Du, W., Han, Y., Chen, S.: Privacy-preserving multivariate statistical analysis: linear regression and classification. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 222–233 (2004)
Durham, E., Xue, Y., Kantarcioglu, M., Malin, B.: Quantifying the correctness, computational complexity, and security of privacy-preserving string comparators for record linkage. Inf. Fusion 13(4), 245–259 (2012)
Fienberg, S.E., Nardi, Y., Slavković, A.B.: Valid statistical analysis for logistic regression with multiple sources. In: Gal, C.S., Kantor, P.B., Lesk, M.E. (eds.) ISIPS 2008. LNCS, vol. 5661, pp. 82–94. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10233-2_8
Gessendorfer, J., Beste, J., Drechsler, J., Sakshaug, J.W.: Statistical matching as a supplement to record linkage: a valuable method to tackle nonconsent bias? J. Official Stat. 34(4), 909–933 (2018)
Ghosh, J., Reiter, J.P., Karr, A.F.: Secure computation with horizontally partitioned data using adaptive regression splines. Comput. Stat. Data Anal. 51(12), 5813–5820 (2007)
Heß, P., Jannsen, S., Leber, U.: Digitalisierung und berufliche Weiterbildung: Beschäftigte, deren Tätigkeiten durch Technologien ersetzbar sind, bilden sich seltener weiter. IAB-Kurzbericht 16/2019 (2019)
Jacobebbinghaus, P., Seth, S.: Linked-Employer-Employee-Daten des IAB: LIAB - Querschnittmodell 2, 1993–2008. FDZ-Datenreport, Institute for Employment Research, Nuremberg, Germany (2010)
Karr, A., Lin, X., Reiter, J., Sanil, A.: Privacy preserving analysis of vertically partitioned data using secure matrix products. J. Official Stat. 25, 125–138 (2009)
Karr, A.F., Lin, X., Sanil, A.P., Reiter, J.P.: Secure regression on distributed databases. J. Comput. Graph. Stat. 14(2), 263–279 (2005)
Lin, X., Karr, A.F.: Privacy-preserving maximum likelihood estimation for distributed data. J. Priv. Confidentiality, 1(2) (2010)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. Wiley, New York (2002)
Oaxaca, R.: Male-female wage differentials in urban labor markets. Int. Econ. Rev. 14(3), 693–709 (1973). http://www.jstor.org/stable/2525981
Rao, F.Y., Cao, J., Bertino, E., Kantarcioglu, M.: Hybrid private record linkage: separating differentially private synopses from matching records. ACM Trans. Priv. Secur. (TOPS) 22(3), 1–36 (2019)
Sakshaug, J.W., Huber, M.: An evaluation of panel nonresponse and linkage consent bias in a survey of employees in Germany. J. Surv. Stat. Method. 4(1), 71–93 (2015)
Sakshaug, J.W., Kreuter, F.: Assessing the magnitude of non-consent biases in linked survey and administrative data. Surv. Res. Methods 6, 113–122 (2012)
Sanil, A.P., Karr, A.F., Lin, X., Reiter, J.P.: Privacy preserving regression modelling via distributed computation. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 677–682 (2004)
Schnell, R.: An efficient privacy-preserving record linkage technique for administrative data and censuses. Stat. J. IAOS 30(3), 263–270 (2014)
Verykios, V.S., Christen, P.: Privacy-preserving record linkage. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 3(5), 321–332 (2013)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Drechsler, J., Klein, B. (2020). Secure Matrix Computation: A Viable Alternative to Record Linkage?. In: Domingo-Ferrer, J., Muralidhar, K. (eds) Privacy in Statistical Databases. PSD 2020. Lecture Notes in Computer Science(), vol 12276. Springer, Cham. https://doi.org/10.1007/978-3-030-57521-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-57521-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57520-5
Online ISBN: 978-3-030-57521-2
eBook Packages: Computer ScienceComputer Science (R0)