Privacy Parameter Variation Using RAPPOR on a Malware Dataset
Abstract
Stricter data protection regulations and the poor application of privacy protection techniques have resulted in a requirement for data-driven companies to adopt new methods of analysing sensitive user data. The RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) method adds parameterised noise, which must be carefully selected to maintain adequate privacy without losing analytical value. This paper applies RAPPOR privacy parameter variations against a public dataset containing a list of running Android applications data. The dataset is filtered and sampled into small (10,000); medium (100,000); and large (1,200,000) sample sizes while applying RAPPOR with ? = 10; 1.0; and 0.1 (respectively low; medium; high privacy guarantees). Also, in order to observe detailed variations within high to medium privacy guarantees (? = 0.5 to 1.0), a second experiment is conducted by progressively.
Cite
@article{arxiv.1907.10387,
title = {Privacy Parameter Variation Using RAPPOR on a Malware Dataset},
author = {Peter Aaby and Juanjo Mata De Acuna and Richard Macfarlane and William J Buchanan},
journal= {arXiv preprint arXiv:1907.10387},
year = {2019}
}