Big Data and Sensitive Data

  • Kurt NielsenEmail author
Part of the Studies in Big Data book series (SBD, volume 42)


Big Data provides a tremendous amount of detailed data for improved decision making, from overall strategic decisions, to automated operational micro-decisions. Directly, or with the right analytical methods, these data may reveal private information such as preferences and choices, as well as bargaining positions. Therefore, these data may be both personal or of strategic importance to companies, which may distort the value of Big Data. Consequently, privacy-preserving use of such data has been a long-standing challenge, but today this can be effectively addressed by modern cryptography. One class of solutions makes data itself anonymous, although this degrades the value of the data. Another class allows confidential use of the actual data by Computation on Encrypted Data (CoED). This chapter describes how CoED can be used for privacy-preserving statistics and how it may distort existing trustee institutions and foster new types of data collaborations and business models. The chapter provides an introduction to CoED, and presents CoED applications for collaborative statistics when applied to financial risk assessment in banks and directly to the banks’ customers. Another application shows how MPC can be used to gather high quality data from, for example,. national statistics into online services without compromising confidentiality.


Collaborative Statistics General Data Protection Regulation (GDPR) Credit Scoring High-risk Borrowers Danish Bank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been partially supported by the EU through the FP7 project PRACTICE and by the Danish Industry Foundation through the project “Big Data by Security”.


  1. 1.
    H. Varian, Economic mechanism design for computerized agents, in First USENIX Workshop on Electronic Commerce (1995)Google Scholar
  2. 2.
    V. Costan, S. Devadas, Intel SGX Explained (Cryptology ePrint Archive, Report 2016/086)Google Scholar
  3. 3.
    A. Gibbard, Manipulation of voting schemes: a general result. Econometrica 41, 587–601 (1973)MathSciNetCrossRefGoogle Scholar
  4. 4.
    R.B. Myerson, Incentives compatibility and the bargaining Problem. Econometrica 47, 61–73 (1979)MathSciNetCrossRefGoogle Scholar
  5. 5.
    A. Shamir, How to share a secret. Commun. ACM 22(11), 612–613 (1979)MathSciNetCrossRefGoogle Scholar
  6. 6.
    D. Chaum, C. Crepeau, I.B. Damgaard, Multiparty unconditionally secure protocols (extended abstract), in 20th ACM STOC, Chicago, Illinois, USA, 24 May 1988 (ACM Press, 1988) pp. 11–19Google Scholar
  7. 7.
    P. Bogetoft, I.B. Damgaard, T. Jacobsen, K. Nielsen, J.I. Pagter, T. Toft (2005), Secure computation, economy, and trust—A generic solution for secure auctions with real-world applications. Basic Research in Computer Science Report RS-05-18Google Scholar
  8. 8.
    P. Bogetoft, D.L. Christensen, I.B. Damgaard, M. Geisler, T. Jakobsen, M. Krigaard, J.D. Nielsen, J.B. Nielsen, K. Nielsen, J. Pagter, M.I. Schwartzbach, T. Toft, Secure Multiparty Computation Goes Live. Lecture Notes in Computer Science 5628, 325–343 (2009)CrossRefGoogle Scholar
  9. 9.
    D. Malkhi, N. Nisan, B. Pinkas, Y. Sella, Fairplay—A secure two-party computation system, in Proceedings of the 13th USENIX Security Symposium (2004), pp. 287–302Google Scholar
  10. 10.
    P. Bogetoft, K. Boye, H. Neergaard-Petersen, K. Nielsen, Reallocating sugar beet contracts: can sugar production survive in Denmark? Eur. Rev. Agric. Econ. 34(1), 1–20 (2007)CrossRefGoogle Scholar
  11. 11.
    B. Pinkas, T. Schneider, N.P. Smart, S.C. Williams, Secure Two-Party Computation Is Practical (Asiacrypt, 2009)CrossRefGoogle Scholar
  12. 12.
    A. Shelat, C. Shen, Two-output Secure Computation With Malicious Adversaries (Euroscript, 2011)CrossRefGoogle Scholar
  13. 13.
    J.B. Nielsen, P.S. Nordholt, C. Orlandi, S.S. Burra, A New Approach to Practical Active-Secure Two-Party Computation (Crypto, 2012)Google Scholar
  14. 14.
    T.K. Frederiksen, J.B. Nielsen, Fast and Maliciously Secure Two-Party Computation Using the GPU (ACNS, 2013)CrossRefGoogle Scholar
  15. 15.
    T.K. Frederiksen, J.B. Nielsen, Faster Maliciously Secure Two-Party Computation Using the GPU (SCN, 2014)Google Scholar
  16. 16.
    Y. Lindell, B. Riva, Blazing Fast 2PC in the Offline/Online Setting with Security for Malicious Adversaries (CCS, 2015)Google Scholar
  17. 17.
    B.N. Nielsen, T. Schneider, R. Trifiletti, Constant Round Maliciously Secure 2PC with Function-independent Preprocessing using LEGO (NDSS, 2017)Google Scholar
  18. 18.
    I. Damgaard, K.L. Damgaard, K. Nielsen, P.S. Nordholt, T. Toft, Confidential benchmarking based on multiparty computation. Financial Cryptography and Data Security. Lecture Notes in Computer Science, vol. 9603 (Springer, 2017), pp. 169–187Google Scholar
  19. 19.
    A. Charnes, W.W. Cooper, E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978)MathSciNetCrossRefGoogle Scholar
  20. 20.
    A. Charnes, W.W. Cooper, E. Rhodes, Short communication: measuring the efficiency of decision making units. Eur. J. Oper. Res. 3, 339 (1979)CrossRefGoogle Scholar
  21. 21.
    E. Emrouznejad, B.R. Parker, G. Tavares, Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA (Soc. Econ. Plann, Sci, 2008)Google Scholar
  22. 22.
    P.J. Agrell, P. Bogetoft, J. Tind, DEA and dynamic yardstick competition in Scandinavian electricity distribution. J. Prod. Anal. 23(2), 173–201 (2005)CrossRefGoogle Scholar
  23. 23.
    P. Bogetoft, K. Nielsen, DEA based auctions. Eur. J. Oper. Res. 184(2), 685–700 (2008)CrossRefGoogle Scholar
  24. 24.
    K. Nielsen, T. Toft, Secure relative performance scheme, in Proceedings of Workshop on Internet and Network Economics. LNCS (2007), 48–58Google Scholar
  25. 25.
    A. Cielen, L. Peeters, K. Vanhoof, Bankruptcy prediction using a data envelopment analysis. Eur. J. Oper. Res. 154(2), 526–532 (2004)CrossRefGoogle Scholar
  26. 26.
    J.C. Paradi, M. Asmild, P.C. Simak, Using DEA and worst practice DEA in credit risk evaluation. J. Prod. Anal. 21(2), 153–165 (2004)CrossRefGoogle Scholar
  27. 27.
    I.M. Premachandra, G.S. Bhabra, T. Sueyoshi, DEA as a tool for bankruptcy assessment: a comparative study with logistic regression technique. Eur. J. Oper. Res. 193(2), 412–424 (2009)CrossRefGoogle Scholar
  28. 28.
    R.D. Banker, A. Charnes, W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30, 1078–1092 (1984)CrossRefGoogle Scholar
  29. 29.
    P. Bogetoft, L. Otto, Benchmarking with DEA, SFA, and R (Springer, New York, 2011)CrossRefGoogle Scholar
  30. 30.
    W.W. Cooper, L.M. Seiford, K. Tone, Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 2nd edn. (Springer, 2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Food and Resource EconomicsUniversity of CopenhagenFrederiksberg CDenmark
  2. 2.PartisiaAarhus NDenmark

Personalised recommendations