Ph.D. Seminar – A N K Zaman
Join us Tuesday, May 31 at 2:00pm in Reynolds 219 for the 2nd Ph.D. seminar by Ph.D. Candidate A N K Zaman.
Title
Privacy-Preserving Data Publishing (PPDP) Algorithm for Imposing Suitable Privacy to Increase Data Utility
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 data is shared even anonymously, there is a possibility of de-anonymizing the data, for example, privacy breaches on AOL and Netflix data sets.
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 ϵ-Differential Privacy principles for publishing secure data for sharing. The proposed algorithm is a non-interactive method to publish anonymous synthetic data set. Laplace noise is added to the generalized data set in a suitable way to maximize the data utility. Experiments show that decision tree based classifier produces better results compared to other existing classification works on anonymized data set.
The major contribution of this work is imposing a suitable privacy budget to add noise to produce secure synthetic data sets that prevent data from the re-identification problem, thus, the implemented system produces useful data sets while ensuring the privacy of data donors.
Advisor: Dr. Charlie Obimbo
Advisory Committee: Dr. David Chiu