The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86 overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.
@cite_24 propose an approach in which closed devices can encrypt data together during recording utilizing short range wireless communication to exchange public keys and negotiate encryption key. Only by obtaining all of the permissions from people who encrypt the recording can one decrypts it. @cite_5 @cite_2 present methods that third--party applications such as perceptual and augmented reality applications have access to only higher-level objects such as a skeleton or a face instead of raw sensor feeds. @cite_16 propose a system that gives users control to mark secure regions that the camera have access to, therefore cameras are prevented from capturing sensitive information. Unlike above solutions, our work focuses on protecting bystanders' privacy by respecting their privacy preferences when they are captured in photos.
@cite_24 @cite_5 @cite_16 @cite_2
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The growing popularity of inexpensive, portable recording devices, such as cellular phone cameras and compact digital audio recorders, presents a significant new threat to privacy. We propose a set of technologies that can be integrated into recording devices to provide stronger, more accurately targeted privacy protections than other legal and technical measures now under consideration. Our design is based on an informed consent principle, which it supports by the use of novel devices and protocols that automate negotiations over consent and ensure appropriate safeguards on recorded data. We define the protocols needed for this purpose and establish their security. We also describe a working prototype implementation that safeguards audio recorded by laptop PCs in a wireless network. Perceptual, "context-aware" applications that observe their environment and interact with users via cameras and other sensors are becoming ubiquitous on personal computers, mobile phones, gaming platforms, household robots, and augmented-reality devices. This raises new privacy risks. We describe the design and implementation of DARKLY, a practical privacy protection system for the increasingly common scenario where an untrusted, third-party perceptual application is running on a trusted device. DARKLY is integrated with OpenCV, a popular computer vision library used by such applications to access visual inputs. It deploys multiple privacy protection mechanisms, including access control, algorithmic privacy transforms, and user audit. We evaluate DARKLY on 20 perceptual applications that perform diverse tasks such as image recognition, object tracking, security surveillance, and face detection. These applications run on DARKLY unmodified or with very few modifications and minimal performance overheads vs. native OpenCV. In most cases, privacy enforcement does not reduce the applications' functionality or accuracy. For the rest, we quantify the tradeoff between privacy and utility and demonstrate that utility remains acceptable even with strong privacy protection. Users are increasingly vulnerable to inadvertently leaking sensitive information through cameras. In this paper, we investigate an approach to mitigating the risk of such inadvertent leaks called privacy markers. Privacy markers give users fine-grained control of what visual information an app can access through a device's camera. We present two examples of this approach: PrivateEye, which allows a user to mark regions of a two-dimensional surface as safe to release to an app, and WaveOff, which does the same for three-dimensional objects. We have integrated both systems with Android's camera subsystem. Experiments with our prototype show that a Nexus 5 smartphone can deliver near realtime frame rates while protecting secret information, and a 26-person user study elicited positive feedback on our prototype's speed and ease-of-use.