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Abstract Abstract: Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used, and often blindly trust the app developers. In this paper, we present IoTWATcH, a dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We have designed and built IoTWATcH through a comprehensive IoT privacy survey addressing the privacy needs of users. IoTWATCH operates in four phases: (a) it provides users with an interface to specify their privacy preferences at app install time, (b) it adds extra logic to an app’s source code to collect both IoT data and their recipients at runtime, (c) it uses Natural Language Processing (NLP) techniques to construct a model that classifies IoT app data into intuitive privacy labels, and (d) it informs the users when their preferences do not match the privacy labels, exposing sensitive data leaks to users. We implemented and evaluated IoTWATcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWATcH yields an average 94.25% accuracy in classifying IoT app data into privacy labels with only 105 ms additional latency to an app’s execution.
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SpaceMediator: Leveraging Authorization Policies to Prevent Spatial and Privacy Attacks in Mobile Augmented Reality
Mobile Augmented Reality (MAR) is a portable, powerful, and suitable technology that integrates digital content, e.g., 3D virtual objects, into the physical world, which not only has been implemented for multiple intents such as shopping, entertainment, gaming, etc., but it is also expected to grow at a tremendous rate in the upcoming years. Unfortunately, the applications that implement MAR, hereby referred to as MAR-Apps, bear security issues, which have been imaged in worldwide incidents such as robberies, which has led authorities to ban MAR-Apps at specific locations. Existing problems with MAR-Apps can be classified into three categories: first, Space Invasion, which implies the intrusive modification through MAR of sensitive spaces, e.g., hospitals, memorials, etc. Second, Space Affectation, which involves the degradation of users' experience via interaction with undesirable MAR or malicious entities. Finally, MAR-Apps mishandling sensitive data leads to Privacy Leaks. To alleviate these concerns, we present an approach for Policy-Governed MAR-Apps, which allows end-users to fully control under what circumstances, e.g., their presence inside a given sensitive space, digital content may be displayed by MAR-Apps. Through SpaceMediator, a proof-of-concept MAR-App that imitates the well-known and successful MAR-App Pokemon GO, we evaluated our approach through a user study with 40 participants, who recognized and prevented the issues just described with success rates as high as 92.50%. Furthermore, there is an enriched interest in Policy-Governed MAR-Apps as 87.50% of participants agreed with it, and 82.50% would use it to implement content-based restrictions in MAR-Apps These promising results encourage the adoption of our solution in future MAR-Apps.
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- PAR ID:
- 10432743
- Date Published:
- Journal Name:
- SACMAT '23: Proceedings of the 28th ACM Symposium on Access Control Models and Technologies
- Page Range / eLocation ID:
- 79 to 90
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3589608.3593839
