There is currently a tug-of-war going on surrounding data releases. On one side, there are many strong reasons pulling to release data to other parties: business factors, freedom of information rules, and scientific sharing agreements. On the other side, concerns about individual privacy pull back, and seek to limit releases. Privacy technologies such as differential privacy have been proposed to resolve this deadlock, and there has been much study of how to perform private data release of data in various forms. The focus of such works has been largely on the data owner: what process should they apply to ensure that the released data preserves privacy whilst still capturing the input data distribution accurately. Almost no attention has been paid to the needs of the data user, who wants to make use of the released data within their existing suite of tools and data. The difficulty of making use of data releases is a major stumbling block for the widespread adoption of data privacy technologies. In this paper, instead of proposing new privacy mechanisms for data publishing, we consider the whole data release process, from the data owner to the data user. We lay out a set of principles for privacy tool design that highlights the requirements for interoperability, extensibility and scalability. We put these into practice with UMicS, an end-to-end prototype system to control the release and use of private data. An overarching tenet is that it should be possible to integrate the released data into the data user's systems with the minimum of change and cost. We describe how to instantiate UMicS in a variety of usage scenarios. We show how using data modeling techniques from machine learning can improve the utility, in particular when combined with background knowledge that the data user may possess. We implement UMicS, and evaluate it over a selection of data sets and release cases. We see that UMicS allows for very effective use of released data, while upholding our privacy principles.
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