Order preserving encryption techniques are treated as some of the most efficient encryption schemes for securing numeric data in a database. Such schemes are popular because they resolve performance degradation issues, which are significant problems in database encryption. However, in some applications, the order itself is sensitive information, and should be hidden. Conventional order preserving encryption techniques published so far, did not consider this issue. Therefore, in this study, we consider three techniques that protect the order information and also show good performance. The three methods hide the data order such that comparison operators can be handled efficiently and performance degradation can be prevented. Our methods work on the top of an order preserving encryption scheme and enhance the security of data. Experimental results demonstrate the efficiency and effectiveness of the proposed three methods.

The existing k-dominant skyline solutions are restricted to centralized query processors, limiting scalability, and imposing a single point of failure. To overcome those problems in this paper, we propose the computation and maintenance algorithms for spatial k-dominant skyline query processing in large-scale distributed environment. Where the underlying dataset is partitioned into geographically distant computing core (personal computer) that are connected to the coordinator (server). Our proposed techniques preserve the spatial k-dominant computation object itself into a serialized form. This preservation is done in client's core after completing a computational job successfully. When the issue of maintenance comes in action, preserve data object retrieves and use for computation. This procedure eliminates the necessity of intermediate re-send and re-computation of k-dominant skyline for the maintenance issue. Thus, we quantify the gain of data transferring consecutively into different cores to maximize the overall gain as well as the query or balancing the load on different cores fairly. Extensive performance study shows that proposed algorithms are efficient and robust to different data distributions.