Bayesian Networks (BNs) have received significant attention in various academic and industrial applications, such as modeling knowledge in image processing, engineering, medicine and bio-informatics. Preserving the pr...
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Bayesian Networks (BNs) have received significant attention in various academic and industrial applications, such as modeling knowledge in image processing, engineering, medicine and bio-informatics. Preserving the privacy of sensitive data, owned by different parties, is often a critical issue. However, in many practical applications, BNs must train from data that gradually becomes available at different period of times, on which the traditional batch learning algorithms are not suitable or applicable. In this paper, an algorithm based on a new and efficient version of Sufficient Statistics is proposed for incremental learning with BNs. The standard kappa 2 algorithm is also modified to be utilized inside the incremental learning algorithm. Next, some secure building blocks such as secure comparison, and factorial, which are resistant against colluding attacks and could be applied securely over public channels like internet, are presented to be used inside the main protocol. Then a privacy-preserving protocol is proposed for incremental learning of BNs, in which the structure and probabilities are estimated incrementally from homogeneously distributed and gradually available data among two or multi-parties. Finally, security and complexity analysis along with the experimental results are presented to compare with the batch algorithm and to show its performance and applicability in real world applications. (C) 2013 Elsevier B. V. All rights reserved.
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy ...
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ISBN:
(纸本)9781424417391
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individual's private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.
data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Some commercial tools...
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ISBN:
(纸本)9781424427505
data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Some commercial tools for collecting network traffic data exist, such as SNORT. The traffic data collected from the network using these tools, however, usually doesn't fit the format requirement of the input data for datamining systems. Thus transforming the network traffic data into the required format is mandate for a datamining system to induce network intrusion detection rules. In this paper, collecting the network packet information using SNORT is introduced, storing the collected data into the MySq1 database is presented, and selecting the significant data in the database and transforming them to the format of input data for a datamining system See5 is discussed. The data collection, selection, and transformation approaches illustrated in this paper have been used in the Information Fusion in Sensor Based Intrusion Detection System that is being under development in our Computer Security Research Laboratory. The system framework is briefly introduced and the preliminary results for data collection from multiple resources are illustrated.
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