A Wavelet‐Based Approach to Preserve Privacy for Classification Mining*

Bapna, Sanjay; Gangopadhyay, Aryya

doi:10.1111/j.1540-5414.2006.00141.x

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…Uhmn, Kim, Kim, Cho, and Cheong (2007) presented SVM as one of the techniques to predict the susceptibility of the liver disease–chronic hepatitis–from single nucleotide polymorphism data. It has been compared with NB (Bapna & Gangopadhay, 2006). We further add SVM as a method in this study.…”

Section: Alternative Methods and Lp Classifiermentioning

confidence: 99%

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

et al. 2018

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In the last couple of decades, data analytics‐based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (LP) models for detecting disease incidence. We propose sequential steps of a convex programming algorithm to construct decision boundary functions to classify patterns in disease detection data. We compare the performance of our LP‐based classifier with others (neural network, decision tree, k‐nearest‐neighbor, logistic regression, naïve‐Bayes, and support‐vector‐machine) on four datasets: two different ones for breast cancer, and one each for diabetes and diabetic retinopathy. Statistical tests reveal that the LP classifier did significantly better than the other methods in five out of eight false‐positive and false‐negative test cases. There is not a statistically significant difference in performance in the remaining three tests between the LP classifier and the best alternative method. Most importantly, the LP classifier has significantly superior performance in both diabetes detection and diabetic retinopathy data. The success of the proposed LP classifier results from avoiding “modeling noise” and “memorization of training data.” We recommend that our proposed LP classifier be among the set of classifiers for use in disease detection analytics.

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Section: Alternative Methods and Lp Classifiermentioning

confidence: 99%

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

et al. 2018

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“…5 The issue has become even more pressing with the recent extensive use of data mining techniques for deriving insights for decision making or target marketing (Menon et al 2005;Bapna and Gangopadhyay 2006;Menon and Sarkar 2007). Moreover, the stringent demands placed on the protection of sensitive data such as health care information, according to Health Insurance Portability and Accountability Act (HIPAA) requirements, make this privacy issue practically challenging (Garfinkel et al 2007).…”

Section: S Department Of Commerce's (2000) International Trade Adminmentioning

confidence: 97%

A Survey of Consumer Information Privacy from the Accounting Information Systems Perspective

Kauffman

Lee

Prosch

et al. 2011

Journal of Information Systems

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The information privacy of consumers is an important public policy issue in today's society, and one that businesses, industries, and governments are continuing to struggle with as information technology (IT) innovations create ever-stronger impacts. In this article, we explore consumer information privacy issues and review the results of related prior research. We provide a survey of the literature on information privacy based on our assessment from: (1) the societal and public policy perspective, (2) the business practices perspective, and (3) the individual privacy and consumer behavior perspectives that relate to the disclosure of private information by individuals to firms. From the societal and public policy perspective, we identify a number of research directions that relate to the social welfare implications of personal information surveillance, the safeguards needed at the societal level, and the nature of privacy-enhancing regulations. From the business practices perspective, we suggest other research directions that involve the examination of factors influencing organizational choices of privacy practices. We also offer some ways to discover how organizations can leverage their investments in information privacy. The individual and consumer behavior perspective, in turn, prompts Accounting Information Systems (AIS) researchers to look at the nature of people's beliefs and attitudes about privacy; the ways such attitudes affect their intentions and behaviors; and how individuals' behaviors can be influenced by organizational privacy policies and practices. The main contribution of this article is to understand these issues and identify opportunities for AIS researchers to enrich the current body of knowledge on this critical topic.

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“…Both methods can also keep the Euclidean distance among data objects after distortion, and the original data records cannot be reconstructed if the original values of several records are leaked. A similar wavelet-based approach was proposed in Bapna and Gangopadhyay (2006).…”

Section: Privacy Preserved Data Miningmentioning

confidence: 99%

A Privacy Preserved Data Mining Framework for Customer Relationship Management

Qiu

2008

Journal of Relationship Marketing

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Collecting customer information and analyzing the information using data mining techniques are the primary processes of customer relationship management. One of the important issues in such processes is how to protect the trade secrecy of corporations and privacy of customers contained in the data sets collected and used for the purpose of data mining. In this article, we propose a privacy preserved data mining framework for customer relationship management that not only enables firms to protect the private information but maintains the performance and utility of the data mining analysis as well. We use churn prediction as a case study to show how this framework works in the real world.

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A Wavelet‐Based Approach to Preserve Privacy for Classification Mining*

Cited by 17 publications

References 28 publications

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

A Survey of Consumer Information Privacy from the Accounting Information Systems Perspective

A Privacy Preserved Data Mining Framework for Customer Relationship Management

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