Earticle

Data storage and its security have been a distress, since the development of the several computing capabilities. Any potential data mishandling can escalate to leakage or breaching, resultant of this can be decline or impinging of trust, privacy or economical stance of the related cloud delegates. Cloud computing is a new technological trend that enables outsourcing of data into the cloud aimed towards elimination of sneakernet as there is no need for rudimentary storage of data as previously in confined physical storages. During decryption data will be vulnerable for some instant of time, as the plain text can be expose, contrary to homomorphic encryption which promotes privacy of the secured data by allowing some operation to be performed on the encrypted data, The homomorphic encryption is presently available for traditional system, the same procedure is applied to the cloud data and in transit.

Distributed Control System (DCS) requires communication with external system, which probably causes the invasion of the viruses and malicious programs, finally resulting in the fault of units. One-way physical isolation device is efficient to solve the current threat but not aware of the potential danger. In this paper, a monitoring algorithm is introduced to predict and detect the abnormal communication based of IP address statistics, thus increasing reliability and flexibility of the communication.

An efficient method is proposed to enhance complex background segmentation for pre-processing in palmprint recognition. In this paper, we integrate texture and haze features by applying Laws masks to the image represented in YCbCr color space, and use these features of a patch with its neighborhood information to determine hand and non-hand regions by support vector machine (SVM). Compared with other methods, our algorithm demonstrated robustness to changing illumination and a complex environment and we obtain a relatively clear hand shape with an average accuracy of 94.96%. The images in our experiments are taken with popular mobile phones in our laboratory.

With the development of cloud computing, the interest on spatial database outsourcing has been sharply increasing. Therefore, researches on data encryption methods for protecting location data privacy in outsourced databases have been actively performed. However, the existing data encryption schemes do not consider data distribution when generating data clusters and they use a tree-based index for processing k-nearest neighbor queries. As a result, the performance of query processing algorithm can fluctuate depending on the tree depth. To solve these problems, we propose a density-aware data encryption scheme and a query processing algorithm for database outsourcing. Our density-aware data encryption scheme uses a grid index to generate clusters and transforms the original data information into a bitmap. To provide efficient query processing, we use an algebraic coding based hash index to reduce the data retrieval time. Finally, in performance analysis, we show that the proposed scheme provides better query processing performance and guarantees the privacy of users, compared with the existing schemes.