Aquilis

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).

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

“…Contextual integrity. Application areas of CI include access rights of smartphone applications [61], analysing user posts in online social networks [18], handling user data in cloud storage [27], detecting data leakage in email drafts [50], designing privacy-aware keyboards [32], and recently privacy reasoning capabilities of conversational agents [34]. In our work, we design an agent that is inherently privacy-conscious and gives appropriate answers instead of detecting privacy leakage.…”