Oftentimes in designing privacy preserving algorithms, the primary assumption is that collected datasets will be analysed by the data owners or by data collectors who have explicitly obtained permissions from the data owners to do so on the condition that privacy criteria are adhered to. In this chapter we consider a data owner scenario in which a data owner wishes to anonymise his/her data before it is shared with a third-party analytics service provider and/or data collector. In such scenarios, the data owners might not have the on-device (or in-house) processing power to conduct the data analytics operations. As such, the data analytics and storage tasks must be outsourced to a third-party data service provider with the resources to store and/or provide useful insights from the data. To enable data analytics, and in line with the theme of this monograph, a necessary pre-processing step is to anonymise the data before it is shared. However, since the data anonymisation operation is conducted on a comparatively low-powered and low-processing device, implementations of anonymisation algorithms for these contexts need to be refactored accordingly.