Summary

Background: With increases in spatial information and enabling technologies, location-privacy concerns have been on the rise. A commonly proposed solution in public health involves random perturbation, however consideration for individual dimensions (attributes) has been weak. Objectives: The current study proposes a multidimensional point transform (MPT) that integrates the spatial dimension with other dimensions of interest to comprehensively anonymise data. Methods: The MPT relies on the availability of a base population, a subset patient dataset, and shared dimensions of interest. Perturbation distance and anonymity thresholds are defined, as are allowable dimensional perturbations. A preliminary implementation is presented using sex, age and location as the three dimensions of interest, with a maximum perturbation distance of 1 kilometre and an anonymity threshold of 20%. A synthesised New York county population is used for testing with 1000 iterations for each of 25, 50, 100, 200 and 400 patient dataset sizes. Results: The MPT consistently yielded a mean perturbation distance of 46 metres with no sex or age perturbation required. Displacement of the spatial mean decreased with patient dataset size and averaged 5.6 metres overall. Conclusions: The MPT presents a flexible, customisable and adaptive algorithm for perturbing datasets for public health, allowing tweaking and optimisation of the trade-offs for different datasets and purposes. It is not, however, a substitute for secure and ethical conduct, and a public health framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information is required. The MPT presents an important component of such a framework.