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Secure big data collection and processing: Framework, means and opportunities

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  • Li‐Chun Zhang
  • Gustav Haraldsen

Abstract

Statistical disclosure control is important for the dissemination of statistical outputs. There is an increasing need for greater confidentiality protection during data collection and processing by National Statistical Offices. In particular, various transactions and remote sensing signals are examples of useful but very detailed big data that can be highly sensitive. Moreover, possible conflicts of interest may arise for data suppliers who operate commercially. In this paper, we formulate statistical disclosure control for data collection and processing as an optimisation problem. Even when it is difficult to specify and solve the problem unequivocally, the formulation can still provide the basis for comparing different disclosure control methods. We develop a general compartmented system that adapts and implements non‐perturbative methods in the related fields of linking sensitive data and secure computation. We illustrate how the system can be configured to yield variously required tables and microdata sets with sufficiently low disclosure risks.

Suggested Citation

  • Li‐Chun Zhang & Gustav Haraldsen, 2022. "Secure big data collection and processing: Framework, means and opportunities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1541-1559, October.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:4:p:1541-1559
    DOI: 10.1111/rssa.12836
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    References listed on IDEAS

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    1. C. J. Skinner & M. J. Elliot, 2002. "A measure of disclosure risk for microdata," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 855-867, October.
    2. Skinner, Chris J. & Shlomo, Natalie, 2008. "Assessing identification risk in survey microdata using log-linear models," LSE Research Online Documents on Economics 39112, London School of Economics and Political Science, LSE Library.
    3. Skinner, Chris & Shlomo, Natalie, 2008. "Assessing Identification Risk in Survey Microdata Using Log-Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 989-1001.
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