To improve the quality of medical care and reduce unnecessary medical errors, electronic medical records (EMRs) are widely applied in hospital information systems. However, rapidly increasing EMRs bring heavy storage burden to hospitals. Professional data management service provided by cloud server can save the hospital local storage, and meanwhile, realize EMRs sharing among external researchers. However, the risk of leaking information of patients discourages hospitals to outsource patients’ EMRs to the remote cloud server. In this paper, a secure and efficient cloud storing and sharing method can be achieved by applying the proposed public key authenticated encryption with ciphertext update and keyword search (PAUKS). The proposed PAUKS scheme enables EMRs to be encrypted and queried without decryption, and is secure against inside keyword guessing attacks. Compared with the recently proposed PAEKS in literature, the PAUKS scheme enjoys smaller computation and communication overheads. The required number of trapdoors per query is constant in PAUKS scheme, instead of the linearly expanding as the number of senders increases in PAEKS. Furthermore, an inverted index can be built safely in PAUKS scheme to accelerate the query procedure. Experiment results show that our PAUKS scheme owns a comparable running overhead, but enjoys a higher query efficiency after ciphertexts update.

Cloud storage service provides a convenient way for users to store massive data. To guarantee the integrity of the outsourced data, provable data possession (PDP) schemes for multi-copy data and multi-cloud have been proposed respectively. To avoid a single point of failure and to increase the security of the outsourced data, many users store multiple copies on multiple cloud servers. However, few protocols support multi-copy data and multi-cloud at the same time. Considering that three entities (third-party auditor, cloud organizer and cloud storage servers) participate in the auditing process, it is difficult for users to locate the position of the faults, including entity position, file position and data block corruption position, when the auditor reports data corruption. In this paper we propose a certificateless multi-copy public auditing protocol in multi-cloud storage, which supports to locate the faults entities and corrupted data blocks. Specifically, instead of relying on a trusted centralized judgement entity to deal with the faults detection, smart contracts combined with the binary search technique are utilized to help achieve the reliability of faults localization result and the accuracy of the judgement. Additionally, the proposed protocol supports data dynamics update, including data modification, insertion and deletion. Theoretical analysis and experiment results show that our protocol is efficient and practical.

While cloud computing is relatively mature and its potential benefits well understood by individual, industry and government consumers, a number of security and privacy concerns remain. Unsurprisingly, designing cryptographic solutions to ensure the security of cloud services and the privacy of data outsourced to the cloud remains an ongoing research area. This paper provides a critique of the wide range of cryptographic schemes designed for securing sensitive data in the cloud computing environment, as well as outlining the research opportunities in the use of cryptographic techniques in cloud computing.

Pig behavior recognition and tracking in group-housed livestock are effective aids for health and welfare monitoring in commercial settings. However, due to demanding farm conditions, the targets in the pig videos are heavily occluded and overlapped, and there are illumination changes, which cause error switches of pig identify (ID) in the tracking process and decrease the tracking quality. To solve these problems, this study proposed an improved DeepSORT algorithm for object tracking, which contained three processes. Firstly, two detectors, YOLOX-S and YOLO v5s, were developed to detect pig targets and classify four types of pig behaviors including lying, eating, standing, and other. Then, the improved DeepSORT was developed for pig behavior tracking and reducing error changes of pig ID by improving trajectory processing and data association. Finally, we established the public dataset annotation of group-housed pigs, with 3600 images in a total from 12 videos, which were suitable for pig tracking applications. The advantage of our method includes two aspects. One is that the trajectory processing and data association are improved by aiming at pig-specific scenarios, which are indoor scenes, and the number of pig target objects is stable. This improvement reduces the error switches of pig ID and enhances the stability of the tracking. The other is that the behavior classification information from the detectors is introduced into the tracking algorithm for behavior tracking. In the experiments of pig detection and behavior recognition, the YOLO v5s and YOLOX-S detectors achieved a high precision rate of 99.4% and 98.43%, a recall rate of 99% and 99.23, and a mean average precision (mAP) rate of 99.50% and 99.23%, respectively, with an AP.5:.95 of 89.3% and 87%. In the experiments of pig behavior tracking, the improved DeepSORT algorithm based on YOLOX-S obtained multi-object tracking accuracy (MOTA), ID switches (IDs), and IDF1 of 98.6%,15, and 95.7%, respectively. Compared with DeepSORT, it improved by 1.8% and 6.8% in MOTA and IDF1, respectively, and IDs had a significant decrease, with a decline of 80%. These experiments demonstrate that the improved DeepSORT can achieve pig behavior tracking with stable ID values under commercial conditions and provide scalable technical support for contactless automated pig monitoring.