scispace - formally typeset
Search or ask a question
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.


Papers
More filters
Proceedings ArticleDOI
20 May 2012
TL;DR: The current policy debate surrounding third-party web tracking is surveyed and the FourthParty web measurement platform is presented, to inform researchers with essential background and tools for contributing to public understanding and policy debates about web tracking.
Abstract: In the early days of the web, content was designed and hosted by a single person, group, or organization. No longer. Webpages are increasingly composed of content from myriad unrelated "third-party" websites in the business of advertising, analytics, social networking, and more. Third-party services have tremendous value: they support free content and facilitate web innovation. But third-party services come at a privacy cost: researchers, civil society organizations, and policymakers have increasingly called attention to how third parties can track a user's browsing activities across websites. This paper surveys the current policy debate surrounding third-party web tracking and explains the relevant technology. It also presents the FourthParty web measurement platform and studies we have conducted with it. Our aim is to inform researchers with essential background and tools for contributing to public understanding and policy debates about web tracking.

535 citations

Proceedings ArticleDOI
06 Jun 2005
TL;DR: This paper provides a formal model for the source-location privacy problem in sensor networks and examines the privacy characteristics of different sensor routing protocols, and devised new techniques to enhance source- location privacy that augment these routing protocols.
Abstract: One of the most notable challenges threatening the successful deployment of sensor systems is privacy. Although many privacy-related issues can be addressed by security mechanisms, one sensor network privacy issue that cannot be adequately addressed by network security is source-location privacy. Adversaries may use RF localization techniques to perform hop-by-hop traceback to the source sensor's location. This paper provides a formal model for the source-location privacy problem in sensor networks and examines the privacy characteristics of different sensor routing protocols. We examine two popular classes of routing protocols: the class of flooding protocols, and the class of routing protocols involving only a single path from the source to the sink. While investigating the privacy performance of routing protocols, we considered the tradeoffs between location-privacy and energy consumption. We found that most of the current protocols cannot provide efficient source-location privacy while maintaining desirable system performance. In order to provide efficient and private sensor communications, we devised new techniques to enhance source-location privacy that augment these routing protocols. One of our strategies, a technique we have called phantom routing, has proven flexible and capable of protecting the source's location, while not incurring a noticeable increase in energy overhead. Further, we examined the effect of source mobility on location privacy. We showed that, even with the natural privacy amplification resulting from source mobility, our phantom routing techniques yield improved source-location privacy relative to other routing methods

531 citations

Proceedings ArticleDOI
30 Nov 2010
TL;DR: This paper assesses how security, trust and privacy issues occur in the context of cloud computing and discusses ways in which they may be addressed.
Abstract: Cloud computing is an emerging paradigm for large scale infrastructures. It has the advantage of reducing cost by sharing computing and storage resources, combined with an on-demand provisioning mechanism relying on a pay-per-use business model. These new features have a direct impact on the budgeting of IT budgeting but also affect traditional security, trust and privacy mechanisms. Many of these mechanisms are no longer adequate, but need to be rethought to fit this new paradigm. In this paper we assess how security, trust and privacy issues occur in the context of cloud computing and discuss ways in which they may be addressed.

530 citations

Journal ArticleDOI
TL;DR: This work investigates confidentiality issues of a broad category of rules, the association rules, and presents three strategies and five algorithms for hiding a group of associationrules, which is characterized as sensitive.
Abstract: Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. We investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.

530 citations

Journal ArticleDOI
Lei Xu1, Chunxiao Jiang1, Jian Wang1, Jian Yuan1, Yong Ren1 
TL;DR: This paper identifies four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker, and examines various approaches that can help to protect sensitive information.
Abstract: The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

528 citations


Network Information
Related Topics (5)
The Internet
213.2K papers, 3.8M citations
88% related
Server
79.5K papers, 1.4M citations
85% related
Encryption
98.3K papers, 1.4M citations
84% related
Social network
42.9K papers, 1.5M citations
83% related
Wireless network
122.5K papers, 2.1M citations
82% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023562
20221,226
20211,535
20201,634
20191,255
20181,277