Indian Journal of Science and Technology, Vol 9(21), DOI: 10.17485/ijst/2016/v9i21/95142, June 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 * Author for correspondence 1. Introduction Data publishing is considered as a critical stage in data analysis system. Publishing sensitive data might lead to individual privacy breach. Predictive rules and techniques 1 can help in predicting privacy information easily. hus data anonymization becomes a requirement to avoid sensitive data leakage. Anonymization techniques like Generalization 2,3 , Bucketization 4–6 and Slicing are well known which handle data anonymization in their own way. In general, these techniques manage in manipulating the original data to avoid sensitive data made available for data analysts. In this course of data manipulation, there are always possibilities of data utilization going down. Utilization loss becoming predominant shall directly afect the accuracy of data analysis. In few occasions the analysis results go completely wrong inally unable to solve the very purpose of data mining and publishing. In general there is a strong assumption that privacy and accuracy are trade of features 7 , practically impossible to achieve both. his paper disagrees with the assumption and explains both privacy and accuracy can be achieved by transforming the algorithm based on the need 8 . Open source Orange data mining tool is used to design a new algorithm called Slicing+ which is the successor of Slicing technique explained in 9 . Further two cases are discussed where in the irst case privacy of data is focused and in the second case accuracy of data is focused. In both the cases the other trade of factor still ofers promising results thus giving this Slicing+ technique a new dimension. Initial part of this paper will detail on merits and demerits of Generalization, Bucketization and Slicing techniques. Abstract Objectives: Privacy and accuracy are always trade off factors in the field of data publishing. Ideally both the factors are considered critical for data handling. Privacy loss and accuracy loss need to be maintained low as possible for an efficient data handling system. Authors have come up with various data publishing techniques aiming to achieve balance between these 2 factors. Generalization, Bucketization and Slicing are well known techniques among the list. Unfortunately they have their own limitation in handling privacy and accuracy. Generalization suffers in handling high dimensional data thus experiencing higher utility loss. Bucketization lacks data privacy where parting sensitive and quasi identifier attributes is a challenge. Slicing on the other hand though offers better privacy and accuracy, there is always scope to improve data correlation aiming in reducing utility loss. This paper explains a new technique called Slicing+ which handles privacy and accuracy factors effectively. This new Slicing+ technique looks promising as it offers flexibility for data publisher to decide on how the data need to be published. Data publisher can tune the Slicing+ technique to get data published with better privacy than accuracy or the other way. Algorithms for the two cases are derived and realized using Orange tool. This paper explains analysis done for the first bucket tuples. As an improvement aspect, similar analysis can be done for other buckets and all the bucket tuples merged and reconstructed for complete analysis. This analysis is applied in the medical records. This hybrid slicing technique is rated against Privacy loss and Utility gain factors. Experimental results are analyzed to justify the performance of Slicing+ technique. Keywords: Accuracy, Data Mining, Privacy, Publishing, Slicing Slicing+: An Eicient Privacy Preserving Data Publishing M. Nithya 1,2* and T. Sheela 3 1 Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, India; nithya.cse@sairam.edu.in 2 Department of Computer Science and Engineering Sri Sairam Engineering College, Chennai - 600044, Tamil Nadu, India 3 Department of Information Technology, Sri Sairam Engineering College, Chennai - 600044, Tamil Nadu, India; hod.it@sairam.edu.in