An Overview of Bayesian Network Applications in Uncertain Domains <s> C. Bayesian Networks Learning for PPDM with Reliability <s> We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. This problem has many applications for online collaboration. We present protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. For lists of length k, we obtain O(k) communication overhead and O(k In In k) computation. The protocol for the semi-honest environment is secure in the standard model, while the protocol for the malicious environment is secure in the random oracle model. We also consider the problem of approximating the size of the intersection, show a linear lower-bound for the communication overhead of solving this problem, and provide a suitable secure protocol. Lastly, we investigate other variants of the matching problem, including extending the protocol to the multi-party setting as well as considering the problem of approximate matching. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> C. Bayesian Networks Learning for PPDM with Reliability <s> In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems. <s> BIB002
In Fig. 3, BN   . The learning process of BN can be described over  . To construct BN over components of  , the scoring function of K2 algorithm can be used to find a relationship. The process of scoring function is to find a relationship by maximizing the scores between a component and its parents set (predecessor components). As a result, the overall system behavior based on the given components of a system can be aggregated to make a reliable decision. Also, K2 algorithm based BN is used in PPDM for locating the most frequent (contributing/sensitive) component/node BIB001 ,  to preserve association rules in XML domain BIB002 . The purpose of locating the most frequent component(s) is to perturb the original database with minimum effect. The apriori algorithm based XML association rules are compared with the mined XML rules after perturbation of original database. XML association rules mined from the perturbed database are matched with the original database mined rules according to hiding effect, side effects (lost and ghost/new XML Association rules).