Implementing the Location Privacy Toolkit in the enviroCar App

Introduction

This blog is about the GSoC 2023 project I will work on this summer. It will contain the problem statement, an initial observation of the solution, and my approach toward the problem.

Simport

The SIMPORT project, which focuses on confidentially handling personal location information on mobile devices, has significantly progressed. As part of this project, a location privacy toolkit has been developed. This toolkit, built as an Android library, empowers users to make informed decisions about the location data they share. In this blog post, I will delve into the project’s details, explaining how we plan to implement the location privacy toolkit within the enviroCar app—an application that allows users to track fuel consumption and emissions.

Important Links

Problem Statement

The enviroCar app relies on the native Android location API, which provides coarse-grained location information. While this is functional for determining fuel efficiency, it raises privacy concerns as it allows potential tracking of users’ movements. To address this issue, we aim to integrate the location privacy toolkit into the enviroCar app, enhancing user privacy and control over their location data.

Approach

The location privacy toolkit offers various techniques to obfuscate location data and protect user privacy. These techniques include noise addition and differential privacy, making it significantly more challenging for third parties to track users’ movements. By leveraging these privacy-enhancing features, we aim to implement the location privacy toolkit in the enviroCar app, ensuring users can protect their sensitive information.

The architecture of the Location Toolkit

Project Implementation

The project to integrate the location privacy toolkit into the enviroCar app comprises the following key steps.

Investigating Integration: Our initial task explores how the location privacy toolkit can seamlessly integrate into the enviroCar app. By understanding the toolkit’s functionalities and APIs, we can identify the best approach to incorporate it into the existing app architecture.

Enhancing User Settings: We will design and implement additional settings screens to give users granular control over their shared data. These screens will enable users to select the specific location data they wish to share, ensuring they maintain complete control over their privacy preferences.

Testing and Bug Fixes: Thorough testing is crucial to guarantee the seamless functionality and reliability of the integration. We will rigorously test the implementation, identifying and addressing any potential bugs or issues that may arise during this process.

Contribution to enviroCar: I will contribute the code to the enviroCar project once the implementation is complete and thoroughly tested. Doing so ensures that our work benefits the wider enviroCar community and allows for future enhancements and improvements.

Technology Stack

To accomplish our objectives, we will leverage the following technologies.

Android: The enviroCar app is developed using the Android platform, making it the primary technology for our project.

Java/Kotlin: The project will involve coding in Java, the programming language for Android app development.

Location Privacy Toolkit: We will utilize the location privacy toolkit—an existing Android library—to implement the necessary privacy features.

Conclusion

Implementing the location privacy toolkit in the enviroCar app marks a significant step forward in protecting user privacy and data control. By allowing users to share location data selectively, we enhance their confidence in the app’s usage while safeguarding sensitive information. This project, estimated to take 175 hours, will employ the Android platform, Java, and the location privacy toolkit. As we progress with the project, we are excited to contribute to the enviroCar community and promote responsible handling of personal location information.