SpaceMediator: Leveraging Authorization Policies to Prevent Spatial and Privacy Attacks in Mobile Augmented Reality

被引:1
作者
Claramunt, Luis [1 ]
Rubio-Medrano, Carlos [2 ]
Baek, Jaejong [1 ]
Ahn, Gail-Joon [1 ]
机构
[1] Arizona State Univ, Tempe, AZ 85281 USA
[2] Texas A&M Univ Corpus Christi, Corpus Christi, TX USA
来源
PROCEEDINGS OF THE 28TH ACM SYMPOSIUM ON ACCESS CONTROL MODELS AND TECHNOLOGIES, SACMAT 2023 | 2023年
基金
美国国家科学基金会;
关键词
Attributes; Authorization Policies; Mobile Augmented Reality;
D O I
10.1145/3589608.3593839
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:79 / 90
页数:12
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