Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification

Download
Citations
Altmetric
Author
Farokhi, FDate
2020Source Title
IEEE Transactions on Big DataPublisher
Institute of Electrical and Electronics Engineers (IEEE)University of Melbourne Author/s
Farokhi, FarhadAffiliation
Electrical and Electronic EngineeringMetadata
Show full item recordDocument Type
Journal ArticleCitations
Farokhi, F. (2020). Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification. IEEE Transactions on Big Data, PP (99), https://doi.org/10.1109/tbdata.2019.2963391.Access Status
Open AccessAbstract
We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivated by the Cramér-Rao bound, inverse of the trace of the Fisher information matrix is used as a measure of the privacy. Conditions are established for ensuring that the classifier extracted from the original dataset and the obfuscated one are close to each other (capturing the utility). The optimal noise distribution is determined by maximizing a weighted sum of the measures of privacy and utility. The optimal privacy-preserving noise is proved to achieve local differential privacy. The results are generalized to a broader class of optimization-based supervised machine learning algorithms. Applicability of the methodology is demonstrated on multiple datasets.
Export Reference in RIS Format
Endnote
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
Refworks
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References