In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

Crowd behavior refers to a collective behavior composed of two or more individuals who influence, interact, and depend on each other for a specific goal. Compared with an ordinary crowd behavior, the probability of a dangerous crowd behavior is much smaller. Video-based crowd behavior recognition can be categorized as one multi-label classification task, which is characterized by complex scenes and imbalanced samples. Aimed at tackling problems of imbalanced samples and multi-label task, a classification method of associative subspace is proposed. For a single category (called main category) with fewer samples, this paper generates a special subspace wherein it is relatively easy to distinguish these samples by association with other categories. A classifier that can weaken the main category and strengthen relationship between the main category and other categories is designed in the subspace. Therefore, the main category can contribute to reducing dependence on the number of samples with the above-mentioned classifier in the corresponding subspace. In order to make full use of the relevant information concerning categories, multi-label information is further injected into spatio-temporal features of video action representation. Experiments on a challenging WWW dataset show that both the proposed subspace method and multi-label information fusion mechanism are efficient.