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Coconut: An IDE Plugin for Developing Privacy-Friendly Apps

Authors:

Yuvraj Agarwal,

Jason I. HongAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 4

Article No.: 178, Pages 1 - 35

https://doi.org/10.1145/3287056

Published: 27 December 2018 Publication History

Abstract

Although app developers are responsible for protecting users' privacy, this task can be very challenging. In this paper, we present Coconut, an Android Studio plugin that helps developers handle privacy requirements by engaging developers to think about privacy during the development process and providing real-time feedback on potential privacy issues. We start by presenting new findings based on a series of semi-structured interviews with Android developers, probing into the difficulties with privacy that developers face when building apps. Based on these findings, we implemented a proof-of-concept prototype of Coconut and evaluated it in a controlled lab study with 18 Android developers (including eight professional developers). Our study results suggest that apps developed with Coconut handled privacy concerns better, and the developers that used Coconut had a better understanding of their code's behavior and wrote a better privacy policy for their app. We also found that requiring developers to do a small amount of annotating work regarding their apps' personal data practices during the development process may result in a significant improvement in app privacy.

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Seymour WAbdi NRamokapane KEdu JSuarez-Tangil GSuch JBalzarotti DXu W(2024)Voice app developer experiences with alexa and google assistantProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699182(5035-5052)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699182
Lee HGao LYang SForlizzi JDas SBalzarotti DXu W(2024)"I don't know if we're doing good. I don't know if we're doing bad": investigating how practitioners scope, motivate, and conduct privacy work when developing AI productsProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699173(4873-4890)Online publication date: 14-Aug-2024
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Coconut: An IDE Plugin for Developing Privacy-Friendly Apps

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 4

December 2018

1169 pages

EISSN:2474-9567

DOI:10.1145/3301777

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Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 December 2018

Accepted: 01 October 2018

Revised: 01 August 2018

Received: 01 May 2018

Published in IMWUT Volume 2, Issue 4

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Lee HGao LYang SForlizzi JDas SBalzarotti DXu W(2024)"I don't know if we're doing good. I don't know if we're doing bad": investigating how practitioners scope, motivate, and conduct privacy work when developing AI productsProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699173(4873-4890)Online publication date: 14-Aug-2024
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Ngong IStenger BNear JFeng YKelley PKapadia A(2024)Evaluating the usability of differential privacy tools with data practitionersProceedings of the Twentieth USENIX Conference on Usable Privacy and Security10.5555/3696899.3696901(21-40)Online publication date: 12-Aug-2024
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