Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
نویسندگان
چکیده
منابع مشابه
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
In this work we analyze the sample complexity of classification by differentially private algorithms. Differential privacy is a strong and well-studied notion of privacy introduced by Dwork et al. (2006) that ensures that the output of an algorithm leaks little information about the data point provided by any of the participating individuals. Sample complexity of private PAC and agnostic learni...
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ژورنال
عنوان ژورنال: SIAM Journal on Computing
سال: 2015
ISSN: 0097-5397,1095-7111
DOI: 10.1137/140991844