  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.
