Abstract
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.
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Notes
- 1.
Known weaknesses are physical side-channels and information leakages through memory access patterns, which breaks the security. A sound review of SGX is provided by Costan and Devadas [2].
- 2.
Gartner has MPC (Multiparty Computation) listed as a technology “on the rise” in a recent report on data security in July 2017 https://www.gartner.com/doc/3772083/hype-cycle-data-security.
- 3.
Other important characteristics are the different types of basic operations such as arithmetic or Boolean, and different types of cryptographic technologies such as secret sharing and homomorphic encryption.
- 4.
Another property is fault tolerance, which allows the MPC system to continue to operate if a CP intentionally or unintentionally fails to operate.
- 5.
- 6.
- 7.
- 8.
The banks are typically the lenders with the utmost priority in case of default.
- 9.
- 10.
One example of an ongoing R&D project that focuses on MPC and machine learning is the EU-project SODA (https://www.soda-project.eu).
- 11.
- 12.
This work has a direct link to the final report from the Commission on Evidence-Based Policymaking in the US. This report explores how to increase the availability of data for assessing government programs, without compromising privacy and confidentiality. The report explains how MPC can be part of the solution as currently tested by Statistics Denmark.
- 13.
Furthermore, if the MPC protocol is based on so-called secret sharing, as it is, there is no decryption key that can be broken by brute force, which within the EU means that the GDPR regulation may not apply. This is, however, to be legally assessed by the national data protection agencies, who are the legal interpreters of the GDPR.
- 14.
- 15.
A dictionary or a legislative text typically defines a trustee as an individual or institution in a position of trust.
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Acknowledgements
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”.
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Nielsen, K. (2019). Big Data and Sensitive Data. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_9
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