If cloud data is not deleted in time after expiration, it may lead to unauthorized access and privacy leakage. For above issue, a cloud data assured deletion scheme based on key distribution and ciphertext sampling was proposed. It was composed of the encryption algorithm and Distributed Hash Table (DHT) network. Firstly, the plaintext was encrypted into the ciphertext. The ciphertext was sampled by random sampling algorithm. The incomplete ciphertext was uploaded to the cloud. Secondly, The trust value of each node in the DHT network was evaluated by evaluative method. The encryption key was processed into the subkeys by Shamir secret sharing algorithm, and the subkeys were distributed into the nodes with high trust degree. Finally, the encryption key was automatically deleted by the periodic self-updating function of the DHT network. The ciphertext in the cloud was overwritten by uploading random data through the Hadoop Distributed File System (HDFS)'s interface. Assured deletion of cloud data was done by deleting the encryption key and the ciphertext. The security analysis and performance analysis demonstrate that the proposed scheme is secure and efficient.

Long-term continuous use of computers would bring negative effects on users' health. In order to detect users fatigue level in a non-invasive manner, an approach that is able to measure fatigue level on hand muscle based on the keyboard and mouse events was proposed. The proposed method integrated keying action match, data noise filtering, and feature vector extraction/classification together to collect and analyze the delay characteristics of both keying and hitting actions, upon which the detection of fatigue level on hand muscle could be enabled. With the detected fatigue level, friends belonging to the same virtual community on current social networks could be, in real-time, alerted and persuaded to take a health-conscious way in their daily use of computers. Particularly, an interesting conclusion has been made that there is an obvious negative correlation between keying (hitting) delay and fatigue level of hand muscle. The experimental validation conducted on two-week data collected from 15 participants shows that the proposed method is effective in detecting users fatigue level and distributing fatigue-related health information on social network platform.

In order to solve the preferable selection problem of offensive mine-laying plans for unmanned aerial vehicles, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on connection number was adopted. With reference to the traditional essential factors for evaluation of mine-laying plans, the synthesized parameter of Unmanned Aerial Vehicle (UAV) was also selected as the main index to scale the effect of mine-laying platform. Then the connection number was used to describe the identity-contrary degree based on integrated plan, while the information entropy weight of each factor was calculated. For the reliability of the ranking process, the relative certainty probability power was improved with the stabilization coefficient, which could be used to evaluate and rank the mine-laying plans. The simulation results indicate the availability of this algorithm.