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Volume 12 Issue 1

Bootstrap Differential Privacy

Christine M. O'Keefe^(a),(*), Anne-Sophie Charest^(b)

Transactions on Data Privacy 12:1 (2019) 1 - 28

Abstract, PDF

(a) CSIRO, GPO Box 1700, Canberra ACT 2601 AUSTRALIA.

(b) Département de mathématiques et de statistique, Université Laval, 1045, avenue de la Médecine, Québec G1V 0A6 CANADA.

e-mail:Christine.O'Keefe @csiro.au; anne-sophie.charest @mat.ulaval.ca

Abstract

This paper concerns the challenge of protecting confidentiality while making statistically useful data and analytical outputs available for research and policy analysis. In this context, the confidentiality protection measure known as differential privacy is an attractive methodology because of its clear definition and the strong guarantees that it promises. However, concerns about differential privacy include the possibility that in some situations the guarantees may be so strong that statistical usefulness is unacceptably low. In this paper, we propose an example of a relaxation of differential privacy that allows confidentiality protection to be balanced against statistical usefulness. We give a practical illustration of the relaxation implemented as Laplace noise addition for confidentiality protection of contingency and magnitude tables. Tables are amongst the most common types of output produced by national statistical agencies, and these outputs are often protected by noise addition.

^* Corresponding author.

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