This study explores teenager perceptions towards advertising in the online social networking environment. The future of online social networking sites is dependant upon the continued support of advertisers in this new medium, which is linked to the acceptance of advertising on these sites by their targeted audience. This exploratory study used the qualitative research methods of focus groups and in-depth personal interviews to gain insights from the teenager participants. The literature review in Chapter Two examined the previous research into advertising theories, consumer attitudes and issues such as advertising avoidance, advertising as a service and trust and privacy in the online social networking environment. The teenage consumer was also examined as were the influences of social identity theory. From this literature review eleven propositions were formed which provided a structure to the analysis of the research. Chapter Three outlined the multi-method research approach of using focus groups and in-depth interviews. The key findings were outlined in Chapter Four and Chapter Five provides discussion regarding these findings and the implications for theory and advertising practice. The main findings from this study suggest that teenagers have very high levels of advertising avoidance and are sceptical towards advertising on their online social networking sites. They have an inherent distrust of commercial messages in the online social networking environment; however they are extremely trusting with the information that they disclose online. They believe that if their site is classified as private, then the information disclosed on this site is not accessible to anyone. The study explores the reasons behind these views. This research has resulted in the identification of seven motivations behind online social networking use. A new model of advertising avoidance in the online social networking environment is also presented and discussed. This model makes a contribution towards filling the gap in available research on online social networking sites and advertising perception. The findings of this study have also resulted in the identification of the characteristics of online social networking sites as an advertising medium. The newness of online social networking sites coupled with the enthusiastic adoption of online social networking by the teenage demographic means that this exploratory study will be of interest to both academics and practitioners alike.

Research Doctorate - Doctor of Philosophy (PhD) / Due to advances in information processing technology and storage capacity, nowadays huge amount of data is being collected for various data analyses. Data mining techniques, such as classification, are often applied on these data to extract hidden information. During the whole process of data mining the data get exposed to several parties and such an exposure potentially leads to breaches of individual privacy. This thesis presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis. Our proposal has two main advantages. Firstly, as also suggested by our experimental results the perturbed data set maintains the same or very similar patterns as the original data set, as well as the correlations among attributes. While there are some noise addition techniques that maintain the statistical parameters of the data set, to the best of our knowledge this is the first comprehensive technique that preserves the patterns and thus removes the so called Data Mining Bias from the perturbed data set. Secondly, re-identification of the original records directly depends on the amount of noise added, and in general can be made arbitrarily hard, while still preserving the original patterns in the data set. The only exception to this is the case when an intruder knows enough about the record to learn the confidential class value by applying the classifier. However, this is always possible, even when the original record has not been used in the training data set. In other words, providing that enough noise is added, our technique makes the records from the training set as safe as any other previously unseen records of the same kind. In addition to the above contribution, this thesis also explores the suitability of pre-diction accuracy as a sole indicator of data quality, and proposes technique for clustering both categorical values and records containing such values.

Research Doctorate - Doctor of Philosophy (PhD) / The rapid pace of growth in the field of human genetics has left researchers with many new challenges in the area of security and privacy. To encourage participation and foster trust towards research, it is important to ensure that genetic databases are adequately protected. This task is a particularly challenging one for statistical agencies due to the high prevalence of categorical data contained within statistical genetic databases. The absence of natural ordering makes the application of traditional Statistical Disclosure Control (SDC) methods less straightforward, which is why we have proposed a new noise addition technique for categorical values. The main contributions of the thesis are as follows. We provide a comprehensive analysis of the trust relationships that occur between the different stakeholders in a genetic data warehouse system. We also provide a quantifiable model of trust that allows the database manager to granulate the level of protection based on the amount of trust that exists between the stakeholders. To the best of our knowledge, this is the first time that trust has been applied in the SDC context. We propose a privacy protection framework for genetic databases which is designed to deal with the fact that genetic data warehouses typically contain a high proportion of categorical data. The framework includes the use of a clustering technique which allows for the easier application of traditional noise addition techniques for categorical values. Another important contribution of this thesis is a new similarity measure for categorical values, which aims to capture not only the direct similarity between values, but also some sense of transitive similarity. This novel measure also has possible applications in providing a way of ordering categorical values, so that more traditional SDC methods can be more easily applied to them. Our analysis of experimental results also points to a numerical attribute phenomenon, whereby we typically have high similarity between numerical values that are close together, and where the similarity decreases as the absolute value of the difference between numerical values increases. However, some numerical attributes appear to not behave in a strictly `numerical' way. That is, values which are close together numerically do not always appear very similar. We also provide a novel noise addition technique for categorical values, which employs our similarity measure to partition the values in the data set. Our method - VICUS - then perturbs the original microdata file so that each value is more likely to be changed to another value in the same partition than one from a different partition. The technique helps to ensure that the perturbed microdata file retains data quality while also preserving the privacy of individual records.

Zugl.: Bayreuth, Univ., Diss., 2006

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 75-78). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 22-25).

Theses (M.A.)--Carleton University, 2002. / Includes bibliographical references (p. 109-114). Also available in electronic format on the Internet.

Thesis (Ph.D.) - Carleton University, 2005. / Includes bibliographical references (p. 287-306). Also available in electronic format on the Internet.

Thesis (J.C.L.)--Catholic University of America, 1990. / Includes bibliographical references (leaves 69-88).