A Scanner Darkly: Protecting User Privacy from Perceptual Applications
A Scanner Darkly: Protecting User Privacy from Perceptual Applications
复制标题
黑暗扫描仪：保护用户隐私免受感知应用的影响
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Vitaly Shmatikov
中科院分区：
文献类型：
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作者：
S. Jana;Arvind Narayanan;Vitaly Shmatikov
感知、“情境感知”的应用程序可以观察周围环境，并通过摄像头和其他传感器与用户交互，这些应用程序在个人电脑、移动电话、游戏平台、家用机器人和增强现实设备上变得无处不在。这带来了新的隐私风险。我们描述了DARKLY的设计和实现，这是一个实用的隐私保护系统，用于日益常见的场景，即不受信任的第三方感知应用程序在受信任的设备上运行。DARKLY与OpenCV集成，OpenCV是一种流行的计算机视觉库，用于此类应用程序访问视觉输入。它部署了多种隐私保护机制，包括访问控制、算法隐私转换和用户审计。我们在20个感知应用程序上评估DARKLY，这些应用程序执行不同的任务，如图像识别、目标跟踪、安全监视和人脸检测。与原生OpenCV相比，这些应用程序运行在未经修改的DARKLY上，或者只做了很少的修改，性能开销最小。在大多数情况下，隐私强制执行不会降低应用程序的功能或准确性。对于其余部分，我们量化了隐私和效用之间的权衡，并证明即使具有强大的隐私保护，效用仍然是可接受的。
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.