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Aquilis: Using Contextual Integrity for Privacy Protection on Mobile Devices

Published: 18 December 2020 Publication History

Abstract

Smartphones are nowadays the dominant end-user device. As a result, they have become gateways to all users' communications, including sensitive personal data. In this paper, we present Aquilis, a privacy-preserving system for mobile platforms following the principles of contextual integrity to define the appropriateness of an information flow. Aquilis takes the form of a keyboard that reminds users of potential privacy leakages through a simple three-colour code. Aquilis considers the instantaneous privacy risk related to posting information (Local Sensitivity), the risk induced by repeating information over time (Longitudinal Sensitivity) and on different platforms (Cross-platform Sensitivity). Considering 50% of Aquilis warnings decreases the proportion of inappropriate information by up to 30%. Repeating information over time or in a broader exposure context increases the risk by 340% in a one-to-one context. We develop our own labeled privacy dataset of over 1000 input texts to evaluate Aquilis' accuracy. Aquilis significantly outperforms other state-of-the-art methods (F-1-0.76). Finally, we perform a user study with 35 highly privacy-aware participants. Aquilis privacy metric is close to users' privacy preferences (average divergence of 1.28/5). Users found Aquilis useful (4.41/5), easy to use (4.4/5), and agreed that Aquilis improves their online privacy awareness (4.04/5).

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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 4, Issue 4
December 2020
1356 pages
EISSN:2474-9567
DOI:10.1145/3444864
Issue’s Table of Contents
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Published: 18 December 2020
Published in IMWUT Volume 4, Issue 4

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  1. Contextual Integrity
  2. Mobile Device
  3. Privacy

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