NDLI: A Novel Differential Privacy Approach that Enhances Classification Accuracy

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A Novel Differential Privacy Approach that Enhances Classification Accuracy

Content Provider	ACM Digital Library
Author	Dara, Rozita A. Obimbo, Charlie Zaman, A. N. K.
Abstract	In the recent past, there has been a tremendous increase of large repositories of data, examples being in healthcare data, consumer data from retailers, and airline passenger data. These data are continually being shared with interested parties, either anonymously -- for research purposes, or openly by financial or insurance companies, for decision-making purposes. When is shared anonymously, there is still the possibility of de-anonymizing the data. Privacy Preserving Data Publishing (PPDP) is a way to allow one to share secure data while ensuring protection against identity disclosure of an individual. Generalization of attributes is a technique of data anonymization where an attribute is replaced with a more generalized value. Differential privacy is a technique that ensures the highest level of privacy for a record owner while providing actual information about the data set. This research develops a framework by generalizing attributes of a data set that satisfy differential privacy principles for publishing secure data for sharing. The proposed algorithm is a non-interactive method to publish anonymize data set, and the decision tree classifier showed better results compared to other existing classification works on anonymized data set. In this paper differential privacy refers to ε-differential privacy.
Starting Page	79
Ending Page	84
Page Count	6
File Format	PDF
ISBN	9781450340755
DOI	10.1145/2948992.2949027
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Differential privacy Data classification Data privacy Data anonymization Privacy preserving data publishing
Content Type	Text
Resource Type	Article

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