# Privacy Parameter Variation Using RAPPOR on a Malware Dataset

## TL;DR

This study investigates how varying privacy parameters in RAPPOR affect data utility and privacy guarantees using a large Android app dataset, providing insights for privacy-preserving data analysis.

## Contribution

It systematically evaluates the impact of different RAPPOR privacy parameters across multiple dataset sizes, offering practical guidance for privacy-utility trade-offs.

## Key findings

- Higher privacy settings reduce data utility but enhance privacy guarantees.
- Optimal parameter selection depends on dataset size and desired privacy level.
- Progressive privacy parameter adjustments reveal detailed utility-privacy trade-offs.

## 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.