Preserving privacy between features in distributed estimation
نویسندگان
چکیده
منابع مشابه
Preserving Differential Privacy Between Features in Distributed Estimation
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets that are partitioned between many parties. One important instance of such a distributed setting arises when information about each record in the dataset is held by different data owners (the design matrix is “vertically...
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ژورنال
عنوان ژورنال: Stat
سال: 2018
ISSN: 2049-1573
DOI: 10.1002/sta4.189