An Overview of Bayesian Network Applications in Uncertain Domains <s> IV. BAYESIAN NETWORKS APPLICATION IN PPDM AND <s> This paper is part of a larger study of remote print defect diagnosis. The goals of the integrated print defect diagnosis system are to reduce diagnosis time, streamline communication between customers and call center personnel, increase print defect handling efficiency and boost customer satisfaction through improved service. In this paper, we aggregate the information stemming from two diagnostic tools – Bayesian belief networks and fuzzy logic – to arrive at a more robust diagnostic result than possible from either tool alone. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> IV. BAYESIAN NETWORKS APPLICATION IN PPDM AND <s> As more and more activities are carried out using computers and computer networks, the amount of potentially sensitive data stored by business, governments, and other parties increases. Different parties may wish to benefit from cooperative use of their data, but privacy regulations and other privacy concerns may prevent the parties from sharing their data. Privacy-preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data is not revealed.In this paper, we present a privacy-preserving protocol for a particular data mining task: learning the Bayesian network structure for distributed heterogeneous data. In this setting, two parties owning confidential databases wish to learn the structure of Bayesian network on the combination of their databases without revealing anything about their data to each other. We give an efficient and privacy-preserving version of the K2 algorithm to construct the structure of a Bayesian network for the parties' joint data. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> IV. BAYESIAN NETWORKS APPLICATION IN PPDM AND <s> This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> IV. BAYESIAN NETWORKS APPLICATION IN PPDM AND <s> Construction of learning structures for Bayesian Networks is considered in this work when data is securely maintained by different parties, not willing to reveal their individual private data to each other. We propose a privacy-preserving protocol for Bayesian Network from data which ishomogeneously partitioned among two or more parties by usingK2 algorithm, a heuristic algorithm typically used to construct Bayesian Network. Three secure building blocks are also presented to use inside the main protocol; Secure Exponentiation, Secure Multi-party Factorial and Secure Product Comparison. We have also modified two existing building blocks which are used in this paper, Secure Multi-Party Addition and Multiplication, to improve their resistance against colluding attack. These protocols have the added advantage that they can even be used over public channels. That is, channels over which any party is able to see any messages exchanged between any two or more parties. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> IV. BAYESIAN NETWORKS APPLICATION IN PPDM AND <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> BIB005
SYSTEM RELIABILITY System Reliability Estimation is the calculation of performance of a system to achieve a specified task with the help of probability under stated constraints. Therefore, probability of system behavior is estimated with the use of BN to develop an effective PPDM model BIB003 , BIB001 . PPDM (Privacy Preserving Data Mining) is a challenging research area to minimize the disclosure risks of XML association rules discovered by data mining techniques. The following sub-section gives application of PPDM as a reliable hiding strategy in preserving XML association rules with minimum side effects. BN-based techniques are reviewed to estimate the system behavior and a hidden strategy for XML rules with reliability. Doguc et al.  proposed a BN model for SOE (System Operation Effectiveness) to substantiate relationships between system components and their developments over time. The objective is to extensively monitor a system for detection of abnormalities in an iterative way. Microsoft MSBNX software is used to build model with complex computations. Reliability of an entire system is estimated through BN by Doguc et al. BIB003 . Lower probability values of components are considered to be better while estimating the system behavior using BN. Luo et al.  surveyed the privacy preserving algorithms and classified them into three main categories; Heuristic-based, Data Reconstruction-based Association Rules and Cryptographic-based techniques. However, this survey does not present any privacy preserving rule mining technique based on BN specifically. Iqbal et al. BIB005 proposed to preserve XML Association Rules (XARs) with BN. In BIB005 , sensitive node(s) are identified through BN using mode and maximum probabilistic node(s). These identified sensitive node(s) are used to perturb the largest sized transaction(s) in the original data source. The purpose of modifying the largest size transaction(s) is to keep the minimum perturbation to avoid generating new rules, ghost rules and lost rules with reliability. Despite this, sensitive XARs are also revealed. Wright et al. BIB002 presented a privacy-preserving protocol by modifying K2 scoring function to secure the distributed heterogeneous data by confining accessibility heuristically. Despite this heuristic approach, partial information is still revealed in perspective of relative ordering. In contrary to BIB002 , Samet et al. BIB004 presented an Object-Oriented Bayesian Network (OOBN) protocol for homogeneously partitioned data to secure it over public channels against colluding attacks in an improved way for real world applications.