Big Data and Sensitive Data
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.
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.H. Varian, Economic mechanism design for computerized agents, in First USENIX Workshop on Electronic Commerce (1995)Google Scholar
- 2.V. Costan, S. Devadas, Intel SGX Explained (Cryptology ePrint Archive, Report 2016/086)Google Scholar
- 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.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
- 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
- 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
- 15.T.K. Frederiksen, J.B. Nielsen, Faster Maliciously Secure Two-Party Computation Using the GPU (SCN, 2014)Google Scholar
- 16.Y. Lindell, B. Riva, Blazing Fast 2PC in the Offline/Online Setting with Security for Malicious Adversaries (CCS, 2015)Google Scholar
- 17.B.N. Nielsen, T. Schneider, R. Trifiletti, Constant Round Maliciously Secure 2PC with Function-independent Preprocessing using LEGO (NDSS, 2017)Google Scholar
- 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
- 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
- 24.K. Nielsen, T. Toft, Secure relative performance scheme, in Proceedings of Workshop on Internet and Network Economics. LNCS (2007), 48–58Google Scholar
- 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