document: Personal privacy vs population privacy : learning to attack anonymization Over the last decade great strides have been made in developing techniques to compute functions privately . In particular , Differential Privacy gives strong promises about conclusions that can be drawn about an individual . In contrast , various syntactic methods for providing privacy ( criteria such as k-anonymity and l-diversity ) have been criticized for still allowing private information of an individual to be inferred . In this paper , we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier . We demonstrate that even under Differential Privacy , such classifiers can be used to infer `` private '' attributes accurately in realistic data . We compare this to similar approaches for inference-based attacks on other forms of anonymized data . We show how the efficacy of all these attacks can be measured on the same scale , based on the probability of successfully inferring a private attribute . We observe that the accuracy of inference of private attributes for differentially private data and $ l $ - diverse data can be quite similar .
doc_bio_tags: O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O