Privacy software

About: Privacy software is a research topic. Over the lifetime, 8597 publications have been published within this topic receiving 237304 citations.

Location privacy in pervasive computing

Abstract: As location-aware applications begin to track our movements in the name of convenience, how can we protect our privacy? This article introduces the mix zone-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity. It is based on frequently changing pseudonyms.

Privacy as contextual integrity

Abstract: The practices of public surveillance, which include the monitoring of individuals in public through a variety of media (e.g., video, data, online), are among the least understood and controversial challenges to privacy in an age of information technologies. The fragmentary nature of privacy policy in the United States reflects not only the oppositional pulls of diverse vested interests, but also the ambivalence of unsettled intuitions on mundane phenomena such as shopper cards, closed-circuit television, and biometrics. This Article, which extends earlier work on the problem of privacy in public, explains why some of the prominent theoretical approaches to privacy, which were developed over time to meet traditional privacy challenges, yield unsatisfactory conclusions in the case of public surveillance. It posits a new construct, “contextual integrity,” as an alternative benchmark for privacy, to capture the nature of challenges posed by information technologies. Contextual integrity ties adequate protection for privacy to norms of specific contexts, demanding that information gathering and dissemination be appropriate to that context and obey the governing norms of distribution within it. Building on the idea of “spheres of justice,” developed by political philosopher Michael Walzer, this Article argues that public surveillance violates a right to privacy because it violates contextual integrity; as such, it constitutes injustice and even tyranny.

Security and Privacy Challenges in Cloud Computing Environments

Abstract: Cloud computing is an evolving paradigm with tremendous momentum, but its unique aspects exacerbate security and privacy challenges. This article explores the roadblocks and solutions to providing a trustworthy cloud computing environment.

Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression

Abstract: Today's globally networked society places great demand on the dissemination and sharing of person-specific data. Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. This happens at a time when more and more historically public information is also electronically available. When these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint, even when the sources of the information contains no explicit identifiers, such as name and phone number. In order to protect the anonymity of individuals to whom released data refer, data holders often remove or encrypt explicit identifiers such as names, addresses and phone numbers. However, other distinctive data, which we term quasi-identifiers, often combine unquely and can be linked to publicly available information to re-identify individuals. In this paper we address the problem of releasing person-specific data while, at the same time, safeguarding the anonymity of individuals to whom the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its contents ambiguiously map the information to at least k entities. We illustrate how k-anonymity can be provided by using generalization and suppression techniques. We introduce the concept of minimal generalization, which captures the property of the release process not to distort the data more than needed to achieve k-anonymity. We illustrate possible preference policies to choose among different minimal generalizations. Finally, we present an algorithm and experimental results when an implementation of the algorithm was used to produce releases of real medical information. We also report ont he quality of the released data by measuring precision and completeness of the results for different values of k.