Privacy constraints in regularized convex optimization
نویسندگان
چکیده
Privacy concerns are becoming more important as more personal data moves online. It is therefore of interest to develop versions of data-processing algorithms which can be guaranteed to preserve the privacy of individuals’ data. A general method for creating a privacy-preserving version of a convex optimization problem is described, along with applications to machine learning, statistics, and resource allocation problems.
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عنوان ژورنال:
- CoRR
دوره abs/0907.1413 شماره
صفحات -
تاریخ انتشار 2009