Can You Have Your Cake and Eat It Too? Sharing Healthcare Data Without Compromising Privacy and Confidentiality

One major concern with electronic healthcare data relates to preserving the privacy and confidentiality of such data. The recent Health Insurance Portability and Accountability Act (HIPAA) of 1996 has established national standards for the protection of health information. The National Institutes of Health also requires healthcare researchers to share data regarding their subjects, but without compromising privacy or confidentiality. In this study, we develop a new technique that can be used to effectively mask healthcare data. The technique guarantees that the results of most (parametric) statistical analyses performed on the masked data will be identical to that perform on the original data. The application of the new technique will allow healthcare researchers and healthcare providers to share sensitive data without compromising privacy and confidentiality

Data Shuffling Procedure for Masking Data

A method for data shuffling to preserve data confidentiality is provided. The method comprises masking of particular attributes of a dataset which are to be preserved in confidentiality, followed by a shuffling step comprising sorting the transformed dataset and a transformed confidential attribute in accordance with the same rank order criteria. For normally distributed datasets, transformation may be achieved by general additive data perturbation, followed by generating a normalized perturbed value of the confidential attribute using a conditional distribution of the confidential and non-confidential attribute. In another aspect, a software program for accomplishing the method of the present invention is provided. The method of the invention provides greater security and utility for the data, and increases user comfort by allowing use of the actual data without identifying the origin

Form and Function: How Website Characteristics Impact User Behavior

This research aims to investigate the effects of website elements, visual appeal and ease of use, as central factors in websitetrust formation and subsequent behaviors. Drawing on existing theories and empirical findings in psychology, informationsystems, and human-computer interaction literature, a research model is developed and proposed to explain the relationshipsamong website visual appeal and its ease of use with trust, perceived usefulness, and intention to purchase. To gather data, anonline experiment was conducted to test the proposed model and hypotheses. The findings of the study reveal that eventhough both website visual appeal and ease of use are significant determinants for website trust, visual appeal produces amuch stronger impact

Data Mining for Decision Support

The amount of data collected by businesses today is phenomenal. The analysis of this data is critical as more and more businesses are using this data to analyze their competition, product or market. Data mining is the process of digging through this mass of data to discover information (patterns or new knowledge) that can be critical to decision making in organizations. Data mining has added importance as organizations begin to rely more heavily on this information to make critical decisions. The need for using the right data mining tools effectively to support decision making cannot be overemphasized

Understanding Compliance with Internet Use Policy: An Integrative Model Based on Command-and- Control and Self-Regulatory Approaches

Internet security risks, the leading security threats confronting today’s organizations, often result from employees’ non-compliance with the Internet use policy (IUP). Extant studies on the compliance with security policies have largely ignored the impact of intrinsic motivations on employees’ compliance intention. This paper proposes a theoretical model that integrates an extrinsic sanction-based command-and-control approach with an intrinsic self-regulatory approach to examine employees’ IUP compliance intention. The self-regulatory approach centers on the effect of organizational justice and personal moral beliefs against Internet abuses. The results of this study suggest that the self-regulatory approach is more effective than the sanctionbased command-and-control approach. Organizational justice not only influences IUP compliance intention directly, but also indirectly through fostering favorable personal moral beliefs against Internet abuses

Fool\u27s Gold: An Illustrated Critique of Differential Privacy

Differential privacy has taken the privacy community by storm. Computer scientists developed this technique to allow researchers to submit queries to databases without being able to glean sensitive information about the individuals described in the data. Legal scholars champion differential privacy as a practical solution to the competing interests in research and confidentiality, and policymakers are poised to adopt it as the gold standard for data privacy. It would be a disastrous mistake. This Article provides an illustrated guide to the virtues and pitfalls of differential privacy. While the technique is suitable for a narrow set of research uses, the great majority of analyses would produce results that are beyond absurd--average income in the negative millions or correlations well above 1.0, for example. The legal community mistakenly believes that differential privacy can offer the benefits of data research without sacrificing privacy. In fact, differential privacy will usually produce either very wrong research results or very useless privacy protections. Policymakers and data stewards will have to rely on a mix of approaches--perhaps differential privacy where it is well suited to the task and other disclosure prevention techniques in the great majority of situations where it isn\u27t