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.

Examining Internet privacy policies within the context of user privacy values

Abstract: Internet privacy policies describe an organization's practices on data collection, use, and disclosure. These privacy policies both protect the organization and signal integrity commitment to site visitors. Consumers use the stated website policies to guide browsing and transaction decisions. This paper compares the classes of privacy protection goals (which express desired protection of consumer privacy rights) and vulnerabilities (which potentially threaten consumer privacy) with consumer privacy values. For this study, we looked at privacy policies from nearly 50 websites and surveyed over 1000 Internet users. We examined Internet users' major expectations about website privacy and revealed a notable discrepancy between what privacy policies are currently stating and what users deem most significant. Our findings suggest several implications to privacy managers and software project managers. Results from this study can help managers determine the kinds of policies needed to both satisfy user values and ensure privacy-aware website development efforts.

Aegis: A Single-Chip Secure Processor

Abstract: In this article, we introduce a single-chip secure processor called Aegis. In addition to supporting mechanisms to authenticate the platform and software, our processor incorporates mechanisms to protect the integrity and privacy of applications from physical attacks as well as software attacks. Therefore, physically secure systems can be built using this processor. Two key primitives, physical unclonable functions (PUFs) and off-chip memory protection, enable the physical security of our system. These primitives can also be easily applied to other secure computing systems to enhance their security.

The influence of the informal social learning environment on information privacy policy compliance efficacy and intention

Abstract: Throughout the world, sensitive personal information is now protected by regulatory requirements that have translated into significant new compliance oversight responsibilities for IT managers who ...

Differential Privacy: An Economic Method for Choosing Epsilon

Abstract: Differential privacy is becoming a gold standard notion of privacy, it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of problems. However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter a#x03B5;, which controls the crucial trade off between the strength of the privacy guarantee and the accuracy of the published results. In this paper, we examine the role of these parameters in concrete applications, identifying the key considerations that must be addressed when choosing specific values. This choice requires balancing the interests of two parties with conflicting objectives: the data analyst, who wishes to learn something about the data, and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose a#x03B5; on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.

Privacy-preserving Naïve Bayes classification

Abstract: Privacy-preserving data mining--developing models without seeing the data --- is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data/databases nor the instances to be classified. Naive Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to that baseline, presenting protocols to develop a Naive Bayes classifier on both vertically as well as horizontally partitioned data.