Personal Privacy and Common Goods: A Framework for Balancing Under the National Health Information Privacy Rule

In this Article, we discuss how these principles for balancing apply in a number of important contexts where individually identifiable health data are shared. In Part I, we analyze the modern view favoring autonomy and privacy. In the last several decades, individual autonomy has been used as a justification for preventing sharing of information irrespective of the good to be achieved. Although respect for privacy can sometimes be important for achieving public purposes (e.g., fostering the physician/patient relationship), it can also impair the achievement of goals that are necessary for any healthy and prosperous society. A framework for balancing that strictly favors privacy can lead to reduced efficiencies in clinical care, research, and public health. We reason that society would be better served, and individuals would be only marginally less protected, if privacy rules permitted exchange of data for important public benefits. In Part II, we explain the national health information privacy regulations: (1) what do they cover?; (2) to whom do they apply?; and (3) how do they safeguard personal privacy? Parts III and IV focus on whether the standards adhere, or fail to adhere, to the privacy principles discussed in Part I. In Part III, we examine two autonomy rules established in the national privacy regulations: informed consent (for uses or disclosures of identifiable health data for health-care related purposes) and written authorization (for uses or disclosures of health data for non-health care related purposes). We observe that the informed consent rule is neither informed nor consensual. The rule is likely to thwart the effective management of health organizations without benefiting the individual. Requiring written authorization, on the other hand, protects individual privacy to prevent disclosures to entities that do not perform health-related functions, such as employers and life insurers. In Part IV, we examine various contexts in which data can be shared for public purposes under the national privacy rule: public health, research, law enforcement, familial notification, and commercial marketing. We apply our framework for balancing in each context and observe the relative strengths and weaknesses of the privacy regulations in achieving a fair balance of private and public interests

Simulating dysarthric speech for training data augmentation in clinical speech applications

Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access. As a result, clinical speech applications are typically developed using small data sets with only tens of speakers. In this paper, we propose a method for simulating training data for clinical applications by transforming healthy speech to dysarthric speech using adversarial training. We evaluate the efficacy of our approach using both objective and subjective criteria. We present the transformed samples to five experienced speech-language pathologists (SLPs) and ask them to identify the samples as healthy or dysarthric. The results reveal that the SLPs identify the transformed speech as dysarthric 65% of the time. In a pilot classification experiment, we show that by using the simulated speech samples to balance an existing dataset, the classification accuracy improves by about 10% after data augmentation.Comment: Will appear in Proc. of ICASSP 201

Research on Privacy Paradox in Social Networks Based on Evolutionary Game Theory and Data Mining

In order to obtain social benefits, social networks have started taking benefits from private information of network users. While having increased concerns about the risk of privacy disclosure, users still generally disclosed under high privacy concerns, which directly formed the privacy paradox. The expansion and generalization of privacy paradox indicate that the implementation of privacy protection in social networks is still in a dilemma. Studying and solving the problem of privacy paradox is conducive to ensure the healthy development of social network industry. Based on this, this study has designed a research system that analyzes the privacy paradox of social networks from three dimensions: cause, existence and form. After studying existing research of privacy paradox in social networks, evolutionary game theory is determined to be introduced into the procedure of cause analysis, while data mining is used as a data analysis method for empirical research. Within the whole research process, the evolutionary game model of privacy paradox in social networks is built up first, while the necessary conditions for the generation of privacy paradox is addressed, which is derived from the evolutionary stable strategy. Secondly, the questionnaire survey method is used to collect private data of active users of both Weibo and WeChat. Lastly, Apriori and CHAID algorithm are used to determine the relationship of user privacy concerns, privacy behavior, and other factors, which then confirms the existence of privacy paradox on two social networks and makes a comparison between their forms of privacy paradox in specific. This research systematically makes a useful an in-depth analysis to the privacy paradox in social networks and is meaningful for establishing a hierarchical protection system of users\u27 privacy for enterprises

It Won\u27t Happen To Me! : Self-Disclosure in Online Social Networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions

East–West Perspectives on Privacy, Ethical Pluralism and Global Information Ethics

Information and Communication Technologies (ICTs) are both primary drivers and facilitating technologies of globalization—and thereby, of exponentially expanding possibilities of cross-cultural encounters. Currently, over one billion persons throughout the planet have access to the Web: of these, Asian users constitute 35.8% of the Web population, while Europeans make up 28.3 % of world users—and North Americans only 20.9% (Internet World Stats, 2007). Our histories teach us all too well that such encounters—especially concerning potentially global ethical norms—always run the risk of devolving into more destructive rather than emancipatory events. Speci?cally, these encounters risk pulling us into one of two contradictory positions. First of all, naïve ethnocentrisms too easily issue in imperialisms that remake “the Other” in one’s own image—precisely by eliminating the irreducible differences in norms and practices that de?ne distinctive cultures. Second, these imperialisms thereby inspire a relativistic turn to the sheerly local—precisely for the sake of preserving local identities and cultures. Hence the general problem: how we might foster a cross-cultural communication for a global ICE that steers between the two Manichean polarities of ethnocentric imperialism and fragmenting relativism

Healthy at Work? Unequal Access to Employer Wellness Programs

Reports findings from a March 2009 survey of American workers' views and opinions on employer-provided wellness programs