Enabling Fine-Grained Permissions for Augmented Reality Applications with Recognizers

By Suman Sekhar Jana, David A. Molnar, Alexander Moshchuk, Alan M. Dunn, Benjamin Livshits, Helen J. Wang, Eyal Ofek. 2013

135 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

I-Pic: A Platform for Privacy-Compliant Image Capture (2016, background): “In [36, 37, 38], the authors address privacy concerns in untrusted perceptual and augmented reality applications, by partially processing media stream within the trusted platform, thus denying apps access to the raw media streams.”
A Scanner Darkly: Protecting User Privacy from Perceptual Applications (2013, background): “[16] built a new OS abstraction (recognizers) and a permission system for enforcing fine-grained, least-privilege access to perceptual data by AR applications.”
MarkIt: privacy markers for protecting visual secrets (2014, background): “[7] proposed the Recognizer abstraction for fine grained access control in AR systems.”
World-Driven Access Control for Continuous Sensing (2014, methodology): “Further, this event-driven model allows application developers to write code agnostic to the application’s current permissions [18].”
What You Mark is What Apps See (2016, background): “One fine-grained approach is to apply the principle of least privilege to visual content and give applications access to only the visual information required for their functionality [9, 10].”
FlowFence: Practical Data Protection for Emerging IoT Application Frameworks (2016, background): “built the recognizer OS abstraction and Darkly [39, 40]—systems that enable apps to compute on perceptual data while protecting the user’s privacy.”
Enhancing Lifelogging Privacy by Detecting Screens (2016, background): “In the embedded system security domain, Scanner Darkly [18] and OS Recognizers [17] control how image data is released to untrusted apps to protect against leaks.”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “One way to enforce the security of edge devices is to restrict the sensor data accessed by the AR applications [225].”
Olympus: Sensor Privacy through Utility Aware Obfuscation (2018, unknown): “Many methods of protecting secrets are specifically designed for certain types of private attribute [11, 26, 28] or data types [12, 23, 24].”