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Recently, storage as a service of cloud computing becomes a new trend to access or share files. Once files are stored in cloud, owner can access files seamlessly by personal computer or mobile device. However, owner may worry about confidentiality and integrity of owner's files stored in cloud because cloud service providers are not always trustworthy. Therefore, there are many kinds of data correctness verification methods proposed to prevent cloud service providers from cheating data owners. Among these models for auditing, bilinear pairing can achieve the most efficient way to verify data correctness and batch auditing. Although auditing methods can ensure whether data is stored properly, it is not considered that the data may be a secret data or a data owner does not want to be known by both auditors and cloud service providers. Another important issue is providing dynamic data of auditing in cloud. Wang et al.¹³ proposed a scheme that can provide public auditing and dynamic data, but it still cannot guarantee whether cloud has updated data honestly. For this reason, we propose a dynamic data guarantee and data confidentiality scheme for public auditing in cloud storage service.

In cloud computing services, according to the customized privacy protection policy by the tenant and the sub chunk-confusion based on privacy protection technology, we can partition the tenant’s data into many chunks and confuse the relationships among chunks, which makes the attacker cannot infer tenant’s information by simply combining attributes. But it still has security issues. For example, with the amount of data growing, there may be a few hidden association rules among some attributes of the data chunks. Through these rules, it is possible to get some of the privacy information of the tenant. To address this issue, the paper proposes a privacy protection mechanism based on chunk-confusion privacy protection technology for association rules. The mechanism can detect unidimensional and multidimensional attributes association rules, hide them by adding fake data, re-chunking and re-grouping, and then ensure the privacy of tenant’s data. In addition, this mechanism also provides evaluation formulas. They filter detected association rules, remove the invalid and improve system performance. They also evaluate the effect of privacy protection. The experimental evaluation proves that the mechanism proposed in this paper can better protect the data privacy of tenant and has feasibility and practicality in real world applications.

Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm.