Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches are very expensive in terms of computation and communication costs and, as a result, they do not scale well for ubiquitous deployment. To this end, we propose a general framework for privacy-preserving identification that operates by storing an encrypted version of the suspects’ database at the video cameras. We show that this approach (i) reduces the protocol to a single round of communication between the camera and the server and (ii) speeds up the computation times significantly through the use of input-independent precomputations. We apply our framework to two practical use-cases, namely, face and license plate number recognition. In addition to the identification result, our face recognition protocol discloses some trivial information to the database server; however, this information is not sufficient for the server to infer any meaningful characteristics about the underlying individuals. On the other hand, the license plate recognition protocol is provably secure and can also handle minor character recognition errors that often occur in such systems. We implemented working prototypes of both surveillance systems and our experimental results are very promising. In the case of face recognition, and for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB. Similarly, for a database of 3000 entries, license plate recognition requires only 232 ms and 75 ms at the camera and the server, respectively, while the online communication cost is 375 KB.

大规模视频监控可以成为执法机构的重要工具。为了缓解大规模视频监控的严重隐私问题，研究人员设计了安全且隐私保护的协议，可以将实时信息与嫌疑人的数据库进行匹配。然而，现有方法在计算和通信成本方面非常昂贵，因此，它们不能很好地扩展到无处不在的部署。为此，我们提出了一个隐私保护识别的通用框架，该框架通过在摄像机中存储嫌疑人数据库的加密版本来进行操作。我们表明这种方法 (i) 将协议简化为相机和服务器之间的单轮通信，并且 (ii) 通过使用与输入无关的预计算显着加快了计算时间。我们将我们的框架应用于两个实际用例，即人脸和车牌号识别。除了识别结果，我们的人脸识别协议还向数据库服务器公开了一些琐碎的信息；然而，这些信息不足以让服务器推断出有关潜在个人的任何有意义的特征。另一方面，车牌识别协议可证明是安全的，并且还可以处理此类系统中经常出现的轻微字符识别错误。我们实现了两个监视系统的工作原型，我们的实验结果非常有希望。在人脸识别的情况下，对于100个嫌疑人的数据库，摄像头和服务器的在线计算时间分别为155 ms和34 ms，而在线通信成本仅为12 KB。同样，对于一个有 3000 个条目的数据库，车牌识别在摄像头和服务器端分别只需要 232 毫秒和 75 毫秒，而在线通信成本为 375 KB。