雲端上具有隱私的移動物體即時監測

隨著雲端運算的發展，愈來愈多應用被移到雲端上，因為雲端有更好的穩定性、更容易改變規模的性質。然而，把原始資料直接送給雲端服務提供者，對於我們的隱私有很大的威脅，特別是雲端監控系統，因為他會持續錄製我們的日常生活情形，並且送到雲端來分析。因此，我們很需要具有隱私的影片分析工具，來保護我們的隱私。在這篇論文的初步研究中，我們實作了"具有隱私的移動物體即時監測"在加密過的影片上。移動物體監測是很多應用的核心技術之一，並且可以用來開發許多更進一步的應用，像是物件追蹤、動作辨識等等。要解決在加密域上做運算的問題，其中一個可能的解法是透過同態加密，他可以在加密域上提供和原始域上相對應的運算。然而，由於同態加密並沒有提供好的方法在加密域上比大小，他需要龐大的溝通、計算才能比出加密域上的大小，另外，加密後的資料大小也會膨脹很多倍，因此，它在實際上的應用是不可行的。在這篇論文中，我們提出一個有效的加密系統，它可以達到在加密影片上做即時的分析 (移動物體偵測)。實驗結果證實了我們提出的方法，可以達到和原始影片上做偵測幾乎一樣的效果。

關鍵字

具有隱私的監測系統；即時偵測

並列摘要

With the advance of cloud computing, growing applications have been migrating to the cloud for its robustness and scalability. However, sending raw data to the cloud-based service providers will generally risk our privacy; especially for cloud-based surveillance system, where privacy is one of the major concerns as continuously recording daily life. Thus, privacy-preserving intelligent analytics are in dire needs. In this preliminary research, we investigate real-time privacy-preserving moving object detection in the encrypted cloud-based surveillance videos. Moving object detection is one of the core techniques and can further enable other applications (e.g., object tracking, action recognition, etc.). One possible approach is using homomorphic encryption which provides corresponding operations between unencrypted and encrypted data. However, homomorphic encryption is impractical in real case because of formidable computations and communication for comparison in encryption domain and bulky storage consumption. In this thesis, we propose an efficient and secure encryption framework, which entails real-time analytics (e.g., moving object detection) in encrypted video streams. Experiments confirm that the proposed method can achieve similar accuracy as detection on original raw frames.

並列關鍵字

Privacy-preserving surveillance ； Real-time detection

參考文獻

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國際替代計量

雲端上具有隱私的移動物體即時監測

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