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Healthcare data can contain sensitive, personal, and confidential information that should remain secure. Despite the efforts to protect patient data, security breaches occur and may result in fraud, identity theft, and other damages. Grounded in the theoretical backdrop of integrated system theory, the purpose of this study was to determine the association between data privacy breaches, data storage locations, business associates, covered entities, and number of individuals affected. Study data consisted of secondary breach information retrieved from the Department of Health and Human Services Office of Civil Rights. Loglinear analytical procedures were used to examine U.S. healthcare breach incidents and to derive a 4-way loglinear model. Loglinear analysis procedures included in the model yielded a significance value of 0.000, p > .05 for the both the likelihood ratio and Pearson chi-square statistics indicating that an association among the variables existed. Results showed that over 70% of breaches involve healthcare providers and revealed that security incidents often consist of electronic or other digital information. Findings revealed that threats are evolving and showed that likely factors other than data loss and theft contribute to security events, unwanted exposure, and breach incidents. Research results may impact social change by providing security professionals with a broader understanding of data breaches required to design and implement more secure and effective information security prevention programs. Healthcare leaders might affect social change by utilizing findings to further the security dialogue needed to minimize security risk factors, protect sensitive healthcare data, and reduce breach mitigation and incident response costs.

Cloud-based assistants like the Google Home and the Amazon Alexa have become ubiquitous in our homes. Users can simply communicate with the devices using a smartphone application. There are privacy concerns associated with cloud-based assistants. For example, users do not know what type of information is being sent to the device manufacturer, if the device is stealthily listening to conversations, data retention, or who else has access to the data. Privacy is about perception. The goal of this study is to determine user privacy concerns regarding cloud-based assistants by adopting a quantitative research method. The study used a privacy decision framework that lists three core components, which are technology controls, understanding user privacy preference, and government regulations. The research used Dervin's sensemaking model to describe users' privacy perception using the privacy decision framework and improved on a privacy perception survey instrument from previous dissertations. An understanding of user privacy concerns with cloud-based assistants is required to provide a comprehensive privacy guidance to stakeholders. The significance of this study is in the identification of the privacy perception of users of cloud-based assistants and the extent to which the components of the theoretical framework can impact user privacy perception. The results of this study serve as a guide for device manufacturers and other stakeholders in prioritizing privacy design decisions.

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