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 bulky storage consumption. In this paper, 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.

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資料隱私與機器智能的權衡？這樣的考量似乎早了一點，但是應該也不遠了。

先回答 一個假設的問題。

如果你在一家提供雲端視訊安全監控的公司工作，客戶的視訊資料串流回雲端設備儲存，你知道某位名模（例如林志玲）是公司客戶，你會不會想打開她的監控視訊串流？

幾年前曾經在幾個不同的演講場合詢問這個問題，令我驚訝的是，有相當人數舉手表示會想看看！？

這也難怪雲服務公司必須有嚴謹的資料存取控制機制，避免客戶隱私遭到侵犯。