Privacy Rules Research Articles

Understanding Sorority Women's Privacy Management About Condom Use.

Sorority women are at risk for engaging in unsafe sexual behavior. Fortunately, the positive influence of peer communication about condom use can mitigate the risk these women face. To better understand this communication, this article investigates sorority women's communication about condom use through focus groups, using the lens of the theory of Communication Privacy Management. The results revealed the criteria for privacy rules sorority women use to negotiate privacy and engage in comfortable communication with other women in the sorority. The analysis also revealed an external privacy boundary; the women sought to maintain a good reputation for their social group, and followed explicit and implicit privacy management rules to do so. The implications for this study include better understanding of the personal and collective boundaries of privacy management, and improvements in sorority and college student sexual health programs.

Implementing Privacy Policy: Who Should Do What?

Academic scholarship on privacy has focused on the substantive rules and policies governing the protection of personal data. An extensive literature has debated alternative approaches for defining how private and public institutions can collect and use information about individuals. But, the attention given to the what of U.S. privacy regulation has overshadowed consideration of how and by whom privacy policy should be formulated and implemented. U.S. privacy policy is an amalgam of activity by a myriad of federal, state, and local government agencies. But, the quality of substantive privacy law depends greatly on which agency or agencies are running the show. Unfortunately, such implementation-related matters have been discounted or ignored -- with the clear implication that they only need to be addressed after the real work of developing substantive privacy rules is completed. As things stand, the development and implementation of U.S. privacy policy is compromised by the murky allocation of responsibilities and authority among federal, state, and local governmental entities -- compounded by the inevitable tensions associated with the large number of entities that are active in this regulatory space. These deficiencies have had major adverse consequences, both domestically and internationally. Without substantial upgrades of institutions and infrastructure, privacy law and policy will continue to fall short of what it could (and should) achieve.

Federal and State Preemption of Local Privacy Regulation

In recent years, cities have started to regulate the collection, use, and disclosure of personal data by local government agencies. Much of this regulatory activity involves setting limits on police department acquisition and/or use of surveillance equipment and technology or establishing privacy principles safeguarding all data collection and use by city agencies. Obviously, the prospects for successful local policymaking in the privacy arena (“privacy localism”) extend no further than the preemptive effect of applicable federal and/or state law. If federal electronic surveillance law preempts local surveillance ordinances or city privacy laws, that’s game, set, and match. State preemption of privacy localism poses an even greater threat given that cities derive their power to govern not from any federal sources of law but from state constitutional and statutory provisions as interpreted by state courts. Thus, states can override local privacy initiatives in either of two ways: by enacting laws that preempt local privacy laws or withdrawing city authority to adopt such laws. In short, quite apart from its substantive merits, the success or failure of privacy localism largely rests on cities finding ways to avoid preemption and maintain local power. This Article examines in detail the threat of federal and state overrides of privacy localism. This is a narrow perspective on privacy localism but of vital importance for the reason just given: federal — and especially state — preemption has the capacity to stop local privacy regulation in its tracks. The inquiry into federal preemption of local privacy regulation requires a review of over two dozen federal privacy statutes. This turns out to be quite manageable because so few of them bear directly on local surveillance ordinances or local regulation of city data practices (which are the principle concerns of privacy localism). State preemption is harder to tackle given that there are approximately 700 state privacy laws, which makes for a crowded regulatory arena with a seemingly endless capacity to override local privacy law. The sheer number of state laws requires some simplifying assumptions to guide the state preemption analysis. Because Seattle and New York city are leading the way in regulating local police use of surveillance technologies and local data practices, this Article will mostly focus on Washington and New York state laws regulating (1) video cameras and/or facial recognition, automatic license plate readers (ALPRs), and drones; and (2) government records or personal data collected by government agencies insofar as they overlap with Seattle and New York City’s locally adopted privacy rules.

Reconsidering the duty to warn genetically at-risk relatives.

The duty to warn genetically at-risk relatives of patients is one of the most misunderstood legal and ethical issues affecting clinical genetics. The legal doctrines are often associated with three state appellate court cases beginning in the mid-1990s. Since the HIPAA Privacy Rule went into effect in 2003, the duty to warn must be accomplished by warning the patient of the genetic nature of a diagnosed disorder or genetic risk and the necessity of warning at-risk relatives. Health-care providers are neither required nor permitted to warn at-risk relatives without the consent of their patients. Having warnings issued by the patient most closely aligns with traditional ethical principles and the interests of the parties. Physicians and other health-care providers can assist their patients by preparing jargon-free explanations of the genetic risk and offering consultation or referral services. In the future, the need for warnings is less likely to be triggered by diagnoses and more likely to be based on predictive information derived from genome sequencing and other technologies and data sources.

Enabling Open-Science Initiatives in Clinical Psychology and Psychiatry Without Sacrificing Patients’ Privacy: Current Practices and Future Challenges

The psychological and psychiatric communities are generating data on an ever-increasing scale. To ensure that society reaps the greatest utility in research and clinical care from such rich resources, there is significant interest in wide-scale, open data sharing to foster scientific endeavors. However, it is imperative that such open-science initiatives ensure that data-privacy concerns are adequately addressed. In this article, we focus on these issues in clinical research. We review the privacy risks and then discuss how they can be mitigated through appropriate governance mechanisms that are both social (e.g., the application of data-use agreements) and technological (e.g., de-identification of structured data and unstructured narratives). We also discuss the benefits and drawbacks of these mechanisms, particularly as regards data fidelity. Our focus is on de-identification methods that meet regulatory requirements, such as the Privacy Rule of the Health Insurance Portability and Accountability Act of 1996. To illustrate their potential, we show how the principles we discuss have been applied in a large-scale clinical database and distributed research networks. We close this article with a discussion of challenges in supporting data privacy as open-science initiatives grow in their scale and complexity.

Patient Privacy in the Era of Big Data.

Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules and responsibilities such as requesting and granting only the amount of health information that is necessary for the scientific study. On the other hand, developers of de-identification systems provide guidelines to use different modes of operations to maximize the effectiveness of their tools and the success of de-identification. Institutions with clinical repositories need to follow these rules and guidelines closely to successfully protect patient privacy. To open the gates of big data to scientific communities, healthcare institutions need to be supported in their de-identification and data sharing efforts by the public, scientific communities, and local, state, and federal legislators and government agencies.

Healthcare Marketing: Who? What? How?

The Department of Health and Human Services (HHS) has struggled with how to create a workable prohibition on marketing, i.e. communications to patients regarding specific products or services, within the framework of the Health Information Portability and Accountability Act (HIPAA) Privacy Rule. In response to modifications to the rule in 2013, a prescription refill reminder company, Adheris, challenged the marketing prohibition as an unconstitutional content-based and speaker-based restrictions on speech. In response to the challenge, HHS settled with Adheris, agreeing to delay enforcement and provide additional guidance. This Article evaluates the unresolved constitutional question presented by Adheris v. Sebelius — whether the HIPAA Marketing prohibition constitutes unconstitutional restrictions on speech. The Article discusses the creation of the marketing prohibition from the initial proposed rulemaking in 1999 to the 2013 Health Information Technology for Economic and Clinical Health (HITECH) Omnibus Rule. The Article then reviews applicable First Amendment law, with particular focus on the prescription detailing cases filed by IMS Health which culminated in Sorrell v. IMS Health and the Supreme Court's heightened review of speaker-based speech restrictions. The Article concludes by setting forth whether and how constitutional prohibitions can be created.

De-Identification of Laboratory Reports in STEM

Background: Employing natural language processing and latent semantic analysis, the current work was completed as a constituent part of a larger research project for designing and launching artificial intelligence in the form of deep artificial neural networks. The models were evaluated on a proprietary corpus retrieved from a data warehouse, where it was extracted from MyReviewers, a sophisticated web application purposed for peer review in written communication, which was actively used in several higher education institutions. The corpus of laboratory reports in STEM annotated by instructors and students was used to train the models. Under the Common Rule, research ethics were ensured by protecting the privacy of subjects and maintaining the confidentiality of data, which mandated corpus de-identification.Literature Review: De-identification and pseudonymization of textual data remains an actively studied research question for several decades. Its importance is stipulated by numerous laws and regulations in the United States and internationally with HIPAA Privacy Rule and FERPA.Research Question: Text de-identification requires a significant amount of manual post-processing for eliminating faculty and student names. This work investigated automated and semi-automated methods for de-identifying student and faculty entities while preserving author names in cited sources and reference lists. It was hypothesized that a natural language processing toolkit and an artificial neural network model with named entity recognition capabilities would facilitate text processing and reduce the amount of manual labor required for post-processing after matching essays to a list of users’ names. The suggested techniques were applied with supplied pre-trained models without additional tagging and training. The goal of the study was to evaluate three approaches and find the most efficient one among those using a users’ list, a named entity recognition toolkit, and an artificial neural network.Research Methodology: The current work studied de-identification of STEM laboratory reports and evaluated the performance of the three techniques: brute forth search with a user lists, named entity recognition with the OpenNLP machine learning toolkit, and NeuroNER, an artificial neural network for named entity recognition built on the TensorFlow platform. The complexity of the given task was determined by the dilemma, where names belonging to students, instructors, or teaching assistants must be removed, while the rest of the names (e.g., authors of referenced papers) must be preserved.Results: The evaluation of the three selected methods demonstrated that automating de-identification of STEM lab reports is not possible in the setting, when named entity recognition methods are employed with pre-trained models. The highest results were achieved by the users’ list technique with 0.79 precision, 0.75 recall, and 0.77 F1 measure, which significantly outweighed OpenNLP with 0.06 precision, 0.14 recall, and 0.09 F1, and NeuroNER with 0.14 precision, 0.56 recall, and 0.23 F1.Discussion: Low performance of OpenNLP and NeuroNER toolkits was explained by the complexity of the task and unattainability of customized models due to imposed time constraints. An approach for masking possible de-identification errors is suggested.Conclusion: Unlike multiple cases described in the related work, de-identification of laboratory reports in STEM remained a non-trivial labor-intensive task. Applied out of the box, a machine learning toolkit and an artificial neural network technique did not enhance performance of the brute forth approach based on user list matching.Directions for Future Research: Customized tagging and training on the STEM corpus were presumed to advance outcomes of machine learning and predominantly artificial intelligence methods. Application of other natural language toolkits may lead to deducing a more effective solution.

Have you shot anyone? How combat veterans manage privacy with family and friends

Abstract This qualitative research project employed semi-structured interviews (analyzed with qualitative coding techniques) to examine how (N = 22) male American combat veterans manage privacy. The two-fold purpose of this study was to determine how combat veterans adhere to or deviate from the principles of communication privacy management theory (CPM). Secondly, to provide new knowledge that can shape counseling strategies and transition programs to account for how veterans manage their privacy. Some of the significant findings are that veterans believe that merely participating in combat implicitly creates a collective boundary that all veterans must maintain to protect the group. Secondly, the veterans did not use boundary coordination or privacy rule development. Instead, they relied upon internal rules that helped them craft a disclosure that minimized risk. Veterans reported having dense privacy boundaries by default, even towards members of their own family. These dense boundaries present significant obstacles to therapists working with veterans and their families.

Cooperative trust relaying and privacy preservation via edge-crowdsourcing in social Internet of Things

Social-Internet of Things (S-IoT) fortifies the relationship between computing entities for efficient utilization of resources. S-IoT accentuates on building convivial circles between the devices, which sanction sharing of information without compromising own performance. Trust and privacy are the two factors responsible for the maintenance of these social circles. Trust establishes faith between the entities and privacy supports abstraction of device information. There exist a plethora of approaches, which fixate on trust and privacy in social networks. However, the subsisting solutions rely on a trust scoring system and utilize a single centralized server for these calculations. Such scoring systems are manipulable by eavesdroppers, which engender erroneous reputation leading to high trust values. Considering the desideratum of an efficient trust and privacy-preserving solution, this paper proposes a novel solution in the form of fission computing. The proposed approach relies on the edge-crowd integration for maintenance of trust and preservation of privacy rules in S-IoT. The proposed solution uses crowdsources as mini-edge servers and entropy modeling for defining trust between the entities. Fission managers are responsible for maintaining the privacy rules, which operate for every S-IoT application. The proposed approach is analyzed through numerical simulations by using a safe network as a performance benchmark. Further, the article presents a case study on the detection of fake news sources in S-IoT as an application of the proposed approach.

Females’ (Non)Disclosure of Minority Sexual Identities in the Workplace From a Communication Privacy Management Perspective

In this study, communication privacy management theory (CPM) provides a theoretical framework for investigating the development of privacy rules and strategies for females disclosing a minority sexual identity (MSI) in the workplace. Analysis of in-depth interviews indicated that participants’ rules were influenced primarily by role and relational risk evaluations. Findings nuance CPM’s conceptualization of role risk to identify 3 variations: risk to employment status, professional image, and ability to complete tasks. In addition, connections between workplace relational and role risks and how gender identities influence risk assessments and disclosure are demonstrated. Despite risk, participants disclosed personal information at work and often did so utilizing implicit disclosure strategies in everyday interactions to test confidants’ reactions and normalize their MSI in the workplace.

Introduction to S12.70 speech privacy in healthcare

The Privacy Rule within HIPAA requires that personal identifiable information be kept private. Reasonable safeguards must be taken for electronic, paper, and oral (speech) communications. The S12.70 standard provides a means to design, operate, and enforce to this requirement. We will explore the background and practical application of this standard.

Exploring societal-level privacy rules for talking about miscarriage

Communication privacy management (CPM) theory posits that culturally specific understandings of privacy guide how people manage private information in everyday conversations. We use the context of miscarriage to demonstrate how societal-level expectations about (in)appropriate topics of talk converge with micro-level decisions about privacy rules and privacy boundary management. More specifically, we explore how people’s perceptions of broad social rules about the topic of miscarriage influence their disclosure decisions. Based on interviews with 20 couples who have experienced pregnancy loss, we examined how couples described miscarriage as a topic that is bound by societal-level expectations about whether and how this subject should be discussed in interpersonal conversations. Participants reflected on their perceptions of societal-level privacy rules for protecting information about their miscarriage experiences and described how these rules affected their own privacy management decisions. We discuss these findings in terms of CPM’s theoretical tools for linking macro-level discourses to everyday talk.

Privacy and Security Guaranteed Weighted Association Rule Mining on Outsourced Transactional Data

Privacy and Security Guaranteed Weighted Association Rule Mining on Outsourced Transactional Data

Impact of HIPAA's minimum necessary standard on genomic data sharing.

PurposeThis article provides a brief introduction to the HIPAA Privacy Rule’s minimum necessary standard, which applies to sharing of genomic data, particularly clinical data, following 2013 Privacy Rule revisions.MethodsThis research used the Thomson Reuters Westlaw™ database and law library resources in its legal analysis of the HIPAA privacy tiers and the impact of the minimum necessary standard on genomic data-sharing. We considered relevant example cases of genomic data-sharing needs.ResultsIn a climate of stepped-up HIPAA enforcement, this standard is of concern to laboratories that generate, use, and share genomic information. How data-sharing activities are characterized—whether for research, public health, or clinical interpretation and medical practice support—affects how the minimum necessary standard applies and its overall impact on data access and use.ConclusionThere is no clear regulatory guidance on how to apply HIPAA’s minimum necessary standard when considering the sharing of information in the data-rich environment of genomic testing. Laboratories that perform genomic testing should engage with policy-makers to foster sound, well-informed policies and appropriate characterization of data-sharing activities to minimize adverse impacts on day-to-day workflows.

Compliance meets human resources: Monitoring competence and integrity

The financial crisis has created a new interest among regulators in the individual responsibilities of senior managers of financial institutions. In the UK, this has led to the creation of a new senior management regime for banks and insurance companies in which managers can face individual sanctions, including fines, if they have not taken reasonable steps to prevent regulatory breaches. This has led in turn to an interest in using human resources (HR) documentation and techniques to further compliance. It is unlikely, however, that firms will use HR documentation such as appraisals as the basis of compliance monitoring and sanctions against individuals. This is partly because of privacy rules and other legal safeguards for individuals, and partly because such monitoring can lead to a culture of fear and suspicion that encourages secretive behaviour. Instead, firms are more likely to support senior management responsibility through the more positive use of HR techniques, including: ●● creating clear lines of individual accountability; ●● testing these lines through the use of regulatory case studies and root cause analysis; ●● reinforcing these responsibilities through established appraisal processes; ●● promoting better culture through better incentives, enhanced corporate governance, and the use of more sophisticated approaches to analysing and understanding behaviour within organisations and ●● promoting a greater capacity for compliant and ethical behaviour through a recruitment policy that is more focused on diversity and social sensitivity within the workforce.

Data transfers between the EU and UK post Brexit?

Changes to the UK constitutional and institutional settlement on Brexit day may affect the likelihood of the UK securing an adequacy decision under GDPR. Despite the UK Government claiming that on Brexit day, ‘it will have fully implemented EU [data] privacy rules’ it will have no equivalent of Article 8 of the EU Charter in domestic law. This may undermine efforts to achieve an adequacy ruling due to the decision of the CJEU in Maximillian Schrems v Data Protection Commissioner. The UK’s decision to continue with a data retention regime in Part 4 of the Investigatory Powers Act 2016 could also be at odds with the Article 8, Charter right. Conflict between the domestic legal settlement of the Investigatory Powers Act 2016 and the decision of the CJEU in Tele2 Sverige AB v Post-och telestyrelsen may also imperil an adequacy decision.

An Argumentation Approach for Resolving Privacy Disputes in Online Social Networks

Preserving users’ privacy is important for Web systems. In systems where transactions are managed by a single user, such as e-commerce systems, preserving privacy of the transactions is merely the capability of access control. However, in online social networks, where each transaction is managed by and has effect on others, preserving privacy is difficult. In many cases, the users’ privacy constraints are distributed, expressed in a high-level manner, and would depend on information that only becomes available over interactions with others. Hence, when a content is being shared by a user, others who might be affected by the content should discuss and agree on how the content will be shared online so that none of their privacy constraints are violated. To enable this, we model users of the social networks as agents that represent their users’ privacy constraints as semantic rules. Agents argue with each other on propositions that enable their privacy rules by generating facts and assumptions from their ontology. Moreover, agents can seek help from others by requesting new information to enrich their ontology. Using assumption-based argumentation, agents decide whether a content should be shared or not. We evaluate the applicability of our approach on real-life privacy scenarios in comparison with user surveys.

Recent Developments and Future Changes to Internet Privacy Rules in the EU, the UK and the U.S.

Recent Developments and Future Changes to Internet Privacy Rules in the EU, the UK and the U.S.

The Communication Privacy Management of Adopted Individuals in Their Social Networks: Disclosure Decisions in Light of the Discourse of Biological Normativity

Although adoptees are often stigmatized for their nonnormative identity, adopted individuals must communicate with nonfamily members to integrate their adoptive identity into their definition of self. In the current study, adult adoptees (n = 25) were interviewed about their processes for disclosing their adoptive status in their social networks. Drawing upon communication privacy management (CPM) theory, inductive analyses demonstrated that adoptees created motivational and contextual criteria to enact privacy rules surrounding adoption. Adoptees maintained privacy to avoid messages of “difference,” insensitive comments, imperviousness, and negative opinions about adoption. Adoptees disclosed about their adoption to build relational closeness and to educate/advocate for adoption. These privacy decisions were undergirded by the societal assumption that families are biologically linked or the discourse of biological normativity (Suter et al., 2014).