I-Pic: A Platform for Privacy-Compliant Image Capture

By Paarijaat Aditya, Rijurekha Sen, Peter Druschel, Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele, Bobby Bhattacharjee, Tongyu Wu. 2016

128 citations recorded by the source corpus.

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.

Subfields: Computer Vision and Pattern Recognition, Privacy & Security, Human-Computer Interaction

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “These kind of explanations have motivated, for instance, secure matching cryptographic protocols for photographer and bystanders to preserve privacy [356,421,422] .”
NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, neutral): “[1, 2, 3, 4, 5, 2, 6, 7, 8, 9].”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “Afterwards, many solutions were presented to respect of the privacy of bystanders and non-users [650], [651].”
Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications (2017, background): “provide a denaturing mechanism where specific faces can be obfuscated but their approach requires many seconds of processing per image and is thereby not usable for real-time applications [25].”
What You Mark is What Apps See (2016, background): “I-Pic uses a combination of short-range wireless signaling to disseminate policies and remote multiparty computation to transform captured images [1].”
Protecting Visual Information in Augmented Reality from Malicious Application Developers (2019, background): “Related works address these privacy concerns by taint-tracking and controlling information flow [8– 10, 14, 22] or adding an intermediate layer to preprocess frames and filter out sensitive visual information [4, 12, 20, 21].”
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].”