Hiding Sensitive XML Association Rules With Supervised Learning Technique

Iqbal, Khalid; Asghar, Sohail; Mirza, Abdulrehman

doi:10.4236/iim.2011.36027

Cited by 3 publications

(9 citation statements)

References 34 publications

(71 reference statements)

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“…Iqbal et al [19] presented a PPDM model for minimizing disclosure risk of sensitive XARs (XML Association Rules) with the use of BN. In this model, a BN based sensitive node is identified through K2 algorithm.…”

Section: K Iqbal Et Al / a Central Tendency-based Privacy Preservinmentioning

confidence: 99%

“…The central tendency measures are computed from the related items/nodes obtained through Bayesian networks. Central tendency measures include average, median, geometric mean and harmonic mean and are different from measures used in [19]. A horizontally partitioned itemset is transformed with the help of central tendency measures using Bayesian networks.…”

Section: An Overview Of the Modelmentioning

confidence: 99%

“…The previously mined XML rules containing sensitive node/item either in antecedent or consequent are referred to as sensitive XML association rules. The comparative results of central tendency measures via Bayesian networks over sample datasets and large UCI machine learning dataset are proven to be better than the existing privacy preserving for association rules [19,27,29,37]. The significance of proposed central tendency measures based privacy preserving with the use of Bayesian networks is the elimination of undesirable side effects in the form of new and lost rules.…”

Section: Introductionmentioning

confidence: 96%

“…A number of XML mining techniques have been developed to discover knowledge through association rules such as incremental data mining algorithm [40], querying XML document [6], query recommendation technique with an architectural framework [4], Standardized Data Source Template (SDST) [36], highly adaptive data mining techniques [24] and Hierarchical Layered Structured Pairset (HiLoP) [33]. Privacy preserving in association rule mining has not only been addressed in XML domain [19] using Bayesian networks but also, in finding spatial association rules in image databases [23] and discovery of frequent itemset in distributed environment [43]. The main limitation of securing association rules in XML mining techniques is generation of side effects in the form of new and lost rules.…”

Section: Introductionmentioning

confidence: 99%

“…The process of data mining must ensure preservation of sensitive association rules with significant belief rather than assumptions. Bayesian networks (also known as belief networks) have been widely used in different domains [13,25,28,31,44] including privacy preserving in XML association rule mining [19] with an objective to accomplish consistent accuracy. Bayesian networks represent a probabilistic graphical structure to discover knowledge about an uncertain domain.…”

Section: Introductionmentioning

confidence: 99%

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A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

Iqbal

Yin

Hao

et al. 2014

IDA

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The rationale of XML design is to transfer and store data at different levels. A key feature of these levels in an XML document is to identify its components for additional processing. XML components can expose sensitive information after application of data mining techniques over a shared database. Therefore, privacy preservation of sensitive information must be ensured prior to signify the outcome especially in sensitive XML Association Rules. Privacy issues in XML domain are not exceptionally addressed to determine a solution by the academia in a reliable and precise manner. In this paper, we have proposed a model for identifying sensitive items (nodes) to declare sensitive XML association rules and then to hide them. Bayesian networks-based central tendency measures are applied in declaration of sensitive XML association rules. K2 algorithm is used to generate Bayesian networks to ensure reliability and accuracy in preserving privacy of XML Association Rules. The proposed model is tested and compared using several case studies and large UCI machine learning datasets. The experimental results show improved accuracy and reliability of proposed model without any side effects such as new rules and lost rules. The proposed model uses the same minimum support threshold to find XML Association Rules from the original and transformed data sources. The significance of the proposed model is to minimize an incredible disclosure risk involved in XML association rule mining from external parties in a competitive business environment.

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Section: K Iqbal Et Al / a Central Tendency-based Privacy Preservinmentioning

confidence: 99%

Section: An Overview Of the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

Iqbal

Yin

Hao

et al. 2014

IDA

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The outcome in patients with brain stroke: A deep learning neural network modeling

et al. 2020

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Background: The artificial intelligence field is obtaining ever-increasing interests for enhancing the accuracy of diagnosis and the quality of patient care. Deep learning neural network (DLNN) approach was considered in patients with brain stroke (BS) to predict and classify the outcome by the risk factors. Materials and Methods: A total of 332 patients with BS (mean age: 77.4 [standard deviation: 10.4] years, 50.6% – male) from Imam Khomeini Hospital, Ardabil, Iran, during 2008–2018 participated in this prospective study. Data were gathered from the available documents of the BS registry. Furthermore, the diagnosis of BS was considered based on computerized tomography scans and magnetic resonance imaging. The DLNN strategy was applied to predict the effects of the main risk factors on mortality. The quality of the model was measured by diagnostic indices. Results: The finding of this study for 81 selected models demonstrated that ranges of accuracy, sensitivity, and specificity are 90.5%–99.7%, 83.8%–100%, and 89.8%–99.5%, respectively. Based on the optimal model (tangent hyperbolic activation function with the minimum–maximum hidden units of 10–20, max epochs of 400, momentum of 0.5, and learning rate of 0.1), the most important predictors for BS mortality were time interval after 10 years (accuracy = 92.2%), age category (75.6%), the history of hyperlipoproteinemia (66.9%), and education level (66.9%). The other independent variables are at moderate importance (66.6%) which include sex, employment status, residential place, smoking habits, history of heart disease, cerebrovascular accident type, blood pressure, diabetes, oral contraceptive pill use, and physical activity. Conclusion: The best means for dropping the BS load is effective BS prevention. DLNN strategy showed a surprising presentation in the prediction of BS mortality based on the main risk factors with an excellent diagnostic accuracy. Moreover, the time interval after 10 years, age, the history of hyperlipoproteinemia, and education level are the most important predictors for BS.

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An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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Abstract-Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.Index Terms-Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation. I. INTRODUCTIONUncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information [1]. Nevertheless, uncertainty may also exist in situations that have enough amount of information [2]. Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables [3]- [5]. BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics.Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty [6]. This recognition of BN has several reasons behind it. First, BN encodes the depen...

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Hiding Sensitive XML Association Rules With Supervised Learning Technique

Cited by 3 publications

References 34 publications

A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

A central tendency-based privacy preserving model for sensitive XML association rules using Bayesian networks

The outcome in patients with brain stroke: A deep learning neural network modeling

An Overview of Bayesian Network Applications in Uncertain Domains

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