An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed that is a combination of the benefits of anatomization, and enhanced slicing approach adhering to the principle of k-anonymity and l-diversity for the purpose of dealing with high dimensional data along with multiple sensitive data. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes (SA) and yields two separate tables with non-overlapping attributes. In the enhanced slicing algorithm, vertical partitioning does the grouping of the correlated SA in ST together and thereby minimizes the dimensionality by employing the advanced clustering algorithm. In order to get the optimal size of buckets, tuple partitioning is conducted by MFA. The experimental outcomes indicate that the proposed method can preserve privacy of data with numerous SA. The anatomization approach minimizes the loss of information and slicing algorithm helps in the preservation of correlation and utility which in turn results in reducing the data dimensionality and information loss. The advanced clustering algorithms prove its efficiency by minimizing the time and complexity. Furthermore, this work sticks to the principle of k-anonymity, l-diversity and thus avoids privacy threats like membership, identity and attributes disclosure.

High-quality local code generation is one of the most difficult tasks the eompiler--wrlter faces. Even if register allocation decisions are postponed and common subexpressions are ignored, instruction selection on machines with complex addressing can be quite difficult. Efficient and general algorithms have been developed to do instruction selection, but these algorithms fail to always find optimal solutions. Instruction selection algorithms based on dynamic programming or complete enumeration always find optimal solutions, but seem to be too costly to be practical. This paper describes a new instruction selection algorithm, and its prototype implementation, based on bottom-up tree pattern-matching. This algorithm is both time and jspace efficien% and is capable of doing optimal instruction selection for the DEC VAX-11 with its rich set of addressing modes.

A garbage collection algorithm that permits a reference count storage reclamation scheme to collect circularly linked inaccessible structures is presented. The algorithm requires no additional information beyond that required by a reference count scheme. In particular, it does not require the garbage collector to be able to find pointers outside the heap. The algorithm is most useful for augmenting reference count storage reclamation systems and for implementing storage management systems on top of languages that do not provide their own. It is, however, considerably less efficient in space and time than conventional garbage collection systems.

In this paper, Cluster analysis is a group objects like observations, events etc based on the information that are found in the data describing the objects or their relations. The main goal of the clustering is that the objects in a group will be similar or related to one other and different from (or unrelated to) the objects in other groups. In this paper, proposed a hybrid model of PSABC algorithm. The PSABC algorithm is a combination of Particle Swarm Algorithm (PSO) and Artificial Bee Colony (ABC) Algorithm used for data clustering on benchmark problems.The PSABC algorithm is compared with other existing classification techniques to evaluate the performance of the proposed approach. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that PSABC algorithm can efficiently be used for multivariate data clustering.

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