Abstract—This paper aims to provide enhancements in the privacy preserving model that was published in our previous paper entitled "An Effective Privacy Preserving Model for Databases Using (α, β, k) - Anonymity Model and Lossy Join" [1]. The previous paper includes a model that maintains the privacy of the multiple sensitive data after the publication of the data in two tables: one for QI-tuples and the other for sensitive attributes. This model used the connecting numbers which depend on one of the sensitive attributes as in lossy join technique. The authors found that in some cases there is a problem may arise with retrieving the exact frequency for any of the rest sensitive attributes if they are not included, as a set of attributes in the same tuple in sensitive attributes table. In other words, the frequency of any one of the rest sensitive attributes is different from the existing frequency of the same attribute in original table especially if the researcher doesn’t use all sensitive attributes in the same tuple together as a set. This problem may affect the ability of researchers to utilize the data and consequently affect the research accuracy. This paper proposed a solution for this problem by adding the frequency details in published sensitive data table for the sensitive attributes that are not used in making connecting numbers. The solution will increase the data utility and improve the research accuracy. Index Terms—Privacy Preserving Model, Anatomy Technique, lossy join, Multiple Sensitive Attributes, Connecting Numbers. This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment. For example, “This work was supported in part by the U.S. Department of Commerce under Grant BS123456”. The next few paragraphs should contain the authors’ current affiliations, including current address and e-mail. For example, F. A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305 USA (e- mail: author@ boulder.nist.gov). S. B. Author, Jr., was with Rice University, Houston, TX 77005 USA. He is now with the Department of Physics, Colorado State University, Fort Collins, CO 80523 USA (e-mail: author@lamar.colostate.edu). T. C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA, on leave from the National Research Institute for Metals, Tsukuba, Japan (e-mail: author@nrim.go.jp). I. INTRODUCTION Data mining is an increasingly important technology for extracting useful knowledge hidden in huge collections of data [2-6]. Data Mining also possible defined as an analysis process of large quantities of data in order to discover meaningful patterns and rules. There are, however, negative social perceptions about data mining, among which potential privacy violation and potential discrimination [7, 8]. Any data mining model generally assumes that the underlying data is freely accessible. The former is an unintentional or deliberate disclosure of a user profile or activity data as part of the output of a data mining algorithm or as a result of data sharing. Even removing identifiers data is not secured, and causes linking attacks [9]. For this reason, privacy preserving data mining has been introduced to protect individual privacy. Privacy preserving data mining (PPDM) has become more and more important because it allows sharing of privacy sensitive attributes for analytical purposes. A big number of privacy techniques were developed most of which used the k- anonymity property. K-anonymity is the emerging concept for the protection of released data [10-15]. Anonymity typically refers to the state on individual's personal identity or personally identifiable information, being publically unknown. When released information linked with confidential table may cause data disclosures. Anonymity model introduced to control linking attack. K-anonymity model suggests to convert identifiers (Quasi identifiers, who are responsible for linking attack) in such a manner that adversary doesn’t infer the sensitive attributes related to them. On the other hand, it is difficult for a data publisher to generate anonymous table, when multiple sensitive attributes are present in data set because concentrating to protect one sensitive attribute may cause disclosure of identity due to another one [14]. An attempt to solve that problem was introduced in [1] that includes a proposed model that maintains the privacy of the multiple sensitive attributes. This previous model solves this problem by publication data in two tables: one for QI-tuples and the other for sensitive attributes. It uses the connecting numbers which depend on one of the sensitive attributes. In the previous proposed model in [1], there is a problem may arise if researcher intended to know the frequency of any one of the rest sensitive attributes. The authors found that this Enhanced Privacy Preserving Model for Data Using (α, β, k)-Anonymity Model and Lossy join Abou_el_ela Abdo Hussien 1 , Nagy Ramadan Darwish 2 1 Department of Computer Science, Shaqra University, KSA, 2 Department of Computer and Information Sciences, Institute of Statistical Studies and Research, Cairo University, International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 11, November 2015 60 https://sites.google.com/site/ijcsis/ ISSN 1947-5500