SMMCOA: Maintaining Multiple Correlations between Overlapped Attributes Using Slicing Technique
نویسندگان
چکیده
-Knowledge discovery is the most discussed topic now a day. Data which is collected from various resources are processed through various stages and the output of this process is the knowledge which is previously hidden. Basically data mining is a technique whose outputs are previously unknown and potentially useful information from data. There are several challenges of data mining are scalability, dimensionality, complex and heterogeneous data, data quality, data distribution and ownership, privacy preserving etc. This paper proposed a new model for privacy preserving data publishing named as Maintaining Multiple Correlations between Overlapped Attributes Using Slicing Technique. In slicing data is portioned in both way vertically and horizontally manner and Bucketization is applied in this resultant. This model is used to prevent against attribute correlation distortion. This paper shows the drawbacks of slicing and proposed a novel model to overcome these drawbacks, the proposed system generates uniform and efficient results even for overlapped attributes. Keywords-Privacy Preserving data Mining, Kanonymity, Slicing, Membership Disclosure, Attribute Correlations.
منابع مشابه
Slicing : A Efficient Method For Privacy Preservation In Data Publishing
In this paper we propose and prove a new technique called “Overlapping Slicing” for privacy preservation of high dimensional data. The process of publishing the data in the web, faces many challenges today. The data usually contains the personal information which are personally identifiable to anyone, thus poses the problem of Privacy. Privacy is an important issue in data publishing. Many orga...
متن کاملSuppression Slicing – using l-diversity
An important problem in publishing the data is privately held data about individuals without revealing the sensitive information about them. Several anonymization techniques, such as suppression, bucketization and slicing have been designed for privacy preservation in microdata publishing. Suppression involves not releasing a value at all it leads to the utility loss while the anonymized table ...
متن کاملImproving Privacy And Data Utility For High- Dimensional Data By Using Anonymization Technique
Privacy Preserving is one of the significant methods in data mining to hide the sensitive information. Anonymization techniques like generalization and bucketization have been used for privacy preserving. The main problem with generalization is it is not applicable for high-dimensional data and bucketization technique does not avoid membership disclosure. Slicing is one of the novel techniques ...
متن کاملA Novel Approach to Privacy Preserving Data Publishing Using Slicing Technique
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separatio...
متن کاملChange Impact Analysis Approach in a Class Hierarchy
Change impact analysis is a technique for determining the potential effects of changes on a software system. As software system evolves, changes made to those systems can have unintended impacts elsewhere. Although, object-oriented features such as encapsulation, inheritance, polymorphism, and dynamic binding contribute to the reusability and extensibility of systems. However, we have to face t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013