Topic
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.
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TL;DR: A research model suggests that an individual’s privacy concerns form through a cognitive process involving perceived privacy risk, privacy control, and his or her disposition to value privacy, and individuals’ perceptions of institutional privacy assurances are posited to affect the riskcontrol assessment from information disclosure.
Abstract: Organizational information practices can result in a variety of privacy problems that can increase consumers’ concerns for information privacy. To explore the link between individuals and organizations regarding privacy, we study how institutional privacy assurances such as privacy policies and industry self-regulation can contribute to reducing individual privacy concerns. Drawing on Communication Privacy Management (CPM) theory, we develop a research model suggesting that an individual’s privacy concerns form through a cognitive process involving perceived privacy risk, privacy control, and his or her disposition to value privacy. Furthermore, individuals’ perceptions of institutional privacy assurances -namely, perceived effectiveness of privacy policies and perceived effectiveness of industry privacy self-regulation -are posited to affect the riskcontrol assessment from information disclosure, thus, being an essential component of privacy concerns. We empirically tested the research model through a survey that was administered to 823 users of four different types of websites: 1) electronic commerce sites, 2) social networking sites, 3) financial sites, and 4) healthcare sites. The results provide support for the majority of the hypothesized relationships. The study reported here is novel to the extent that existing empirical research has not explored the link between individuals’ privacy perceptions and institutional privacy assurances. We discuss implications for theory and practice and provide suggestions for future research.
518 citations
20 Aug 2002
TL;DR: This work presents a scheme, based on probabilistic distortion of user data, that can simultaneously provide a high degree of privacy to the user and retain a high level of accuracy in the mining results.
Abstract: Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. We investigate here, with respect to mining association rules, whether users can be encouraged to provide correct information by ensuring that the mining process cannot, with any reasonable degree of certainty, violate their privacy. We present a scheme, based on probabilistic distortion of user data, that can simultaneously provide a high degree of privacy to the user and retain a high level of accuracy in the mining results. The performance of the scheme is validated against representative real and synthetic datasets.
518 citations
TL;DR: A blockchain-based framework for secure, interoperable, and efficient access to medical records by patients, providers, and third parties, while preserving the privacy of patients’ sensitive information is proposed, named Ancile.
517 citations
17 May 2008
TL;DR: In this paper, a new notion of data privacy, called distributional privacy, which is strictly stronger than the prevailing privacy notion, differential privacy, is introduced, and a new lower bound for releasing databases that are useful for halfspace queries over a continuous domain is shown.
Abstract: We demonstrate that, ignoring computational constraints, it is possible to release privacy-preserving databases that are useful for all queries over a discretized domain from any given concept class with polynomial VC-dimension. We show a new lower bound for releasing databases that are useful for halfspace queries over a continuous domain. Despite this, we give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, for a slightly relaxed definition of usefulness. Inspired by learning theory, we introduce a new notion of data privacy, which we call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.
516 citations
TL;DR: The authors obtain a common goal to provide a comprehensive review of the existing security and privacy issues in cloud environments to present the relationships among them, the vulnerabilities that may be exploited by attackers, the threat models, as well as existing defense strategies in a cloud scenario.
Abstract: Recent advances have given rise to the popularity and success of cloud computing. However, when outsourcing the data and business application to a third party causes the security and privacy issues to become a critical concern. Throughout the study at hand, the authors obtain a common goal to provide a comprehensive review of the existing security and privacy issues in cloud environments. We have identified five most representative security and privacy attributes (i.e., confidentiality, integrity, availability, accountability, and privacy-preservability). Beginning with these attributes, we present the relationships among them, the vulnerabilities that may be exploited by attackers, the threat models, as well as existing defense strategies in a cloud scenario. Future research directions are previously determined for each attribute.
513 citations