Internet of Things (IoT) systems have aroused enthusiasm and concerns. Enthusiasm comes from their utilities in people daily life, and concerns may be associated with privacy issues. By using two IoT systems as case-studies, we examine users' privacy beliefs, concerns and attitudes. We focus on four major dimensions: the collection of personal data, the inference of new information, the exchange of information to third parties, and the risk-utility trade-off posed by the features of the system. Altogether, 113 Brazilian individuals answered a survey about such dimensions. Although their perceptions seem to be dependent on the context, there are recurrent patterns. Our results suggest that IoT users can be classified into unconcerned, fundamentalists and pragmatists. Most of them exhibit a pragmatist profile and believe in privacy as a right guaranteed by law. One of the most privacy concerning aspect is the exchange of personal information to third parties. Individuals' perceived risk is negatively correlated with their perceived utility in the features of the system. We discuss practical implications of these results and suggest heuristics to cope with privacy concerns when designing IoT systems.
From a legislation viewpoint, considering the laws in the United States of America, privacy can be defined as the right of an individual to be let alone'' @cite_6 . People, in turn, usually associate the word privacy with a diversity of meanings. Some people believe that privacy is the right to control what information about them may be made public @cite_21 @cite_2 @cite_19 . Other people believe that if someone cares about privacy is because he she is involved in wrongdoing @cite_9 . Privacy is also associated with the states of solitude, intimacy, anonymity, and reserve @cite_1 @cite_19 . Solitude means the physical separation from other individuals. Intimacy is some kind of close relationship between individuals with which information is exchanged. Anonymity is the state of freedom from identification and surveillance. Finally, reserve means the creation of psychological protection against intrusion by other unwanted individuals.
@cite_9 @cite_21 @cite_1 @cite_6 @cite_19 @cite_2
2075476409 2089513810 2103088651 2164649498
Philosophical and legal theories of privacy have long recognized the relationship between privacy and information about persons. They have, however, focused on personal, intimate, and sensitive information, assuming that with public information, and information drawn from public spheres, either privacy norms do not apply, or applying privacy norms is so burdensome as to be morally and legally unjustifiable. Against this preponderant view, I argue that information and communications technology, by facilitating surveillance, by vastly enhancing the collection, storage, and analysis of information, by enabling profiling, data mining and aggregation, has significantly altered the meaning of public information. As a result, a satisfactory legal and philosophical understanding of a right to privacy, capable of protecting the important values at stake in protecting privacy, must incorporate, in addition to traditional aspects of privacy, a degree of protection for privacy in public. Privacy is a concept which received relatively little attention during the rapid growth and spread of information technology through the 1980's and 1990's. Design to make information easily accessible, without particular attention to issues such as whether an individual had a desire or right to control access to and use of particular information was seen as the more pressing goal. We believe that there will be an increasing awareness of a fundamental need to address privacy concerns in information technology, and that doing so will require an understanding of policies that govern information use as well as the development of technologies that can implement such policies. The research reported here describes our efforts to design a privacy management workbench which facilitates privacy policy authoring, implementation, and compliance monitoring. This case study highlights the work of identifying organizational privacy requirements, analyzing existing technology, on-going research to identify approaches that address these requirements, and iteratively designing and validating a prototype with target users for flexible privacy technologies. The prevailing paradigm in Internet privacy literature, treating privacy within a context merely of rights and violations, is inadequate for studying the Internet as a social realm. Following Goffman on self-presentation and Altman's theorizing of privacy as an optimization between competing pressures for disclosure and withdrawal, the author investigates the mechanisms used by a sample (n = 704) of college students, the vast majority users of Facebook and Myspace, to negotiate boundaries between public and private. Findings show little to no relationship between online privacy concerns and information disclosure on online social network sites. Students manage unwanted audience concerns by adjusting profile visibility and using nicknames but not by restricting the information within the profile. Mechanisms analogous to boundary regulation in physical space, such as walls, locks, and doors, are favored; little adaptation is made to the Internet's key features of persistence, searchability, and cross-indexa... The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented (in Section 2) values for each sensitive attribute. In this paper, we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness.” We first present the base model t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We then propose a more flexible privacy model called (n,t)-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.