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Wu, Y., Lyu, Y., Fang, Q., Zheng, G., Yin, H., Shi, Y..  2017.  Protecting Outsourced Data in Semi-Trustworthy Cloud: A Hierarchical System. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). :300–305.

Data outsourcing in cloud is emerging as a successful paradigm that benefits organizations and enterprises with high-performance, low-cost, scalable data storage and sharing services. However, this paradigm also brings forth new challenges for data confidentiality because the outsourced are not under the physic control of the data owners. The existing schemes to achieve the security and usability goal usually apply encryption to the data before outsourcing them to the storage service providers (SSP), and disclose the decryption keys only to authorized user. They cannot ensure the security of data while operating data in cloud where the third-party services are usually semi-trustworthy, and need lots of time to deal with the data. We construct a privacy data management system appending hierarchical access control called HAC-DMS, which can not only assure security but also save plenty of time when updating data in cloud.

Jiao, L., Yin, H., Guo, D., Lyu, Y..  2017.  Heterogeneous Malware Spread Process in Star Network. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). :265–269.

The heterogeneous SIS model for virus spread in any finite size graph characterizes the influence of factors of SIS model and could be analyzed by the extended N-Intertwined model introduced in [1]. We specifically focus on the heterogeneous virus spread in the star network in this paper. The epidemic threshold and the average meta-stable state fraction of infected nodes are derived for virus spread in the star network. Our results illustrate the effect of the factors of SIS model on the steady state infection.