Using the Lehmer Mean to Assess Business Data Protection: Statistical Disclosure Control and the Truncated Moment Problem

Authors

Mark Stander
Julian Stander, School of Engineering, Computing and Mathematics

ORCID

• Julian Stander: 0000-0002-1429-9862

Abstract

Confidential business data needs protection against disclosure. Often this data is protected by releasing sample means, variances and higher power moments. Motivated by statistical disclosure control obligations and the need to publish business data safely, we explain how calculating the Lehmer mean from released power moments can lead to the unwanted disclosure of the largest data value. We explain how similar disclosure can apply to smaller data values and provide an approximate solution to the Truncated Moment Problem. We briefly discuss the Gini mean and the relationship between sample central and raw moments.

Publication Date

2024-12-03

Publication Title

Transactions on Data Privacy

Volume

18

Issue

2

ISSN

1888-5063

Keywords

Gini mean, Lehmer mean, Power moments, Statistical disclosure limitation, Truncated moments problem

First Page

95

Last Page

112

Recommended Citation

Stander, M., & Stander, J. (2024) 'Using the Lehmer Mean to Assess Business Data Protection: Statistical Disclosure Control and the Truncated Moment Problem', Transactions on Data Privacy, 18(2), pp. 95-112. Retrieved from https://pearl.plymouth.ac.uk/secam-research/2065