Preserving privacy between features in distributed estimation
Preserving 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 data sets that are partitioned between many parties. One important instance of such a distributed setting arises when information about each record in the …