"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.
This proposal outlines a research plan that considers the whole data release process, from the data owner to the data user. It lays out a set of principles for privacy tool design that highlight the requirements for interoperability, extensibility and scalability. A number of research goals are identified, around topics of synthetic data generation, correlated data modeling, data utility enhancement, and the application of these to the case of trajectory data. The goal of the project is Doing Anonymization Practically, Privately, Effectively and Reusablely (DAPPER)."
Call for proposal
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