This talk is about the experience of providing privacy when running analytics on users’ personal data. The two-sided market of Cloud Analytics emerged almost accidentally, initially from click-through associated with user's response to search results, and then adopted by many other services, whether web mail or social media. The business model seen by the user is of a free service (storage and tools for photos, video, social media etc). The value to the provider is untrammeled access to the user's data over space and time, allowing upfront income from the ability to run recommenders and targeted adverts, to background market research about who is interested in what information, goods and services, when and where. The value to the user is increased personalisation. This all comes at a cost, both of privacy (and the risk of loss of reputation or even money) for the user, and at the price of running highly expensive data centers for the providers, and increased cost in bandwidth and energy consumption (mobile network costs & device battery life). The attack surface of our lives expands to cover just about everything. This talk will examine several alternative directions that this will evolve in the future. Firstly, we look at a toolchain for traditional cloud processing which offers privacy through careful control of the lifecycle of access to data, processing, and production of results by combining several relatively new techniques. Secondly, we present a fully decentralized approach, on low cost home devices, which can potentially lead to large reduction in risks of loss of confidentiality.