Information privacy

About: Information privacy is a research topic. Over the lifetime, 25412 publications have been published within this topic receiving 579611 citations. The topic is also known as: data privacy & data protection.

The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer Willingness to Disclose Personal Health Information

Abstract: As healthcare becomes increasingly digitized, the promise of improved care enabled by technological advances inevitably must be traded off against any unintended negative consequences. There is little else that is as consequential to an individual as his or her health. In this context, the privacy of one's personal health information has escalated as a matter of significant concern for the public. We pose the question: under what circumstances will individuals be willing to disclose identified personal health information and permit it to be digitized? Using privacy boundary theory and recent developments in the literature related to risk-as-feelings as the core conceptual foundation, we propose and test a model explicating the role played by type of information requested (general health, mental health, genetic), the purpose for which it is to be used (patient care, research, marketing), and the requesting stakeholder (doctors/hospitals, the government, pharmaceutical companies) in an individual's willingness to disclose personal health information. Furthermore, we explore the impact of emotion linked to one's health condition on willingness to disclose. Results from a nationally representative sample of over 1,000 adults underscore the complexity of the health information disclosure decision and show that emotion plays a significant role, highlighting the need for re-examining the timing of consent. Theoretically, the study extends the dominant cognitive-consequentialist approach to privacy by incorporating the role of emotion. It further refines the privacy calculus to incorporate the moderating influence of contextual factors salient in the healthcare setting. The practical implications of this study include an improved understanding of consumer concerns and potential impacts regarding the electronic storage of health information that can be used to craft policy.

Protecting free expression online with Freenet

Abstract: Freenet is a distributed information storage system designed to address information privacy and survivability concerns. Freenet operates as a self-organizing P2P network that pools unused disk space across potentially hundreds of thousands of desktop computers to create a collaborative virtual file system. Freenet employs a completely decentralized architecture. Given that the P2P environment is inherently untrustworthy and unreliable, we must assume that participants could operate maliciously or fail without warning at any time. Therefore, Freenet implements strategies to protect data integrity and prevent privacy leaks in the former instance, and provide for graceful degradation and redundant data availability in the latter. The system is also designed to adapt to usage patterns, automatically replicating and deleting files to make the most effective use of available storage in response to demand.

A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems

Abstract: To provide fine-grained access to different dimensions of the physical world, the data uploading in smart cyber-physical systems suffers novel challenges on both energy conservation and privacy preservation. It is always critical for participants to consume as little energy as possible for data uploading. However, simply pursuing energy efficiency may lead to extreme disclosure of private information, especially when the uploaded contents from participants are more informative than ever. In this article, we propose a novel mechanism for data uploading in smart cyber-physical systems, which considers both energy conservation and privacy preservation. The mechanism preserves privacy by concealing abnormal behaviors of participants, while still achieves an energy-efficient scheme for data uploading by introducing an acceptable number of extra contents. To derive an optimal uploading scheme is proved to be NP-hard. Accordingly, we propose a heuristic algorithm and analyze its effectiveness. The evaluation results towards a real-world dataset demonstrate that the performance of the proposed algorithm is comparable with the optimal results.

International Differences in Information Privacy Concerns: A Global Survey of Consumers

Abstract: We examine three possible explanations for differences in Internet privacy concerns revealed by national regulation: (1) These differences reflect and are related to differences in cultural values described by other research; (2) these differences reflect differences in Internet experience; or (3) they reflect differences in the desires of political institutions without reflecting underlying differences in privacy preferences Using a sample of Internet users from 38 countries matched against the Internet population of the United States, we find support for (1) and (2), suggesting the need for localized privacy policies Privacy concerns decline with Internet experience Controlling for experience, cultural values were associated with differences in privacy concerns These cultural differences are mediated by regulatory differences, although new cultural differences emerge when differences in regulation are harmonized Differences in regulation reflect but also shape country differences Consumers in countries with sectoral regulation have less desire for more privacy regulation

IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?

Abstract: The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.