Thesis (M.S.)--Rutgers University, 2008. / "Graduate Program in Industrial and Systems Engineering." Includes bibliographical references (p. 80-83).

<p>  </p> <p>The rise of Internet of Things (IoT) technologies into the substantial commercial market that it is today comes with several challenges. Not only do these systems face the traditional challenges of security and reliability faced by traditional information technology (IT) products, but they also face the challenge of loss of privacy. The concern of user data privacy is most prevalent when these technologies come into the home environment. In this dissertation quasi-experimental research is conducted on the ability of users to protect private data in a heterogeneous smart home network. For this work the experiments are conducted and verified on eight different smart home devices using network traffic analysis and discourse analysis to identify privacy concerns. The results of the research show that data privacy within the confines of the user’s home often cannot be ensured while maintaining smart home device functionality. This dissertation discusses how those results can inform users and manufacturers alike in the use and development of future smart home technologies to better protect privacy concerns.</p>

<p dir="ltr">Neural network-based machine learning models are now prevalent in our daily lives, from voice assistants~\cite{lopez2018alexa}, to image generation~\cite{ramesh2021zero} and chatbots (e.g., ChatGPT-4~\cite{openai2023gpt4}). These large neural networks are powerful but also raise serious security and privacy concerns, such as whether personal data used to train these models are leaked by these models. One way to understand and address this privacy concern is to study membership inference (MI) attacks and defenses~\cite{shokri2017membership,nasr2019comprehensive}. In MI attacks, an adversary seeks to infer if a given instance was part of the training data. We study the membership inference (MI) attack against classifiers, where the attacker's goal is to determine whether a data instance was used for training the classifier. Through systematic cataloging of existing MI attacks and extensive experimental evaluations of them, we find that a model's vulnerability to MI attacks is tightly related to the generalization gap---the difference between training accuracy and test accuracy. We then propose a defense against MI attacks that aims to close the gap by intentionally reduces the training accuracy. More specifically, the training process attempts to match the training and validation accuracies, by means of a new {\em set regularizer} using the Maximum Mean Discrepancy between the softmax output empirical distributions of the training and validation sets. Our experimental results show that combining this approach with another simple defense (mix-up training) significantly improves state-of-the-art defense against MI attacks, with minimal impact on testing accuracy. </p><p dir="ltr"><br></p><p dir="ltr">Furthermore, we considers the challenge of performing membership inference attacks in a federated learning setting ---for image classification--- where an adversary can only observe the communication between the central node and a single client (a passive white-box attack). Passive attacks are one of the hardest-to-detect attacks, since they can be performed without modifying how the behavior of the central server or its clients, and assumes {\em no access to private data instances}. The key insight of our method is empirically observing that, near parameters that generalize well in test, the gradient of large overparameterized neural network models statistically behave like high-dimensional independent isotropic random vectors. Using this insight, we devise two attacks that are often little impacted by existing and proposed defenses. Finally, we validated the hypothesis that our attack depends on the overparametrization by showing that increasing the level of overparametrization (without changing the neural network architecture) positively correlates with our attack effectiveness.</p><p dir="ltr">Finally, we observe that training instances have different degrees of vulnerability to MI attacks. Most instances will have low loss even when not included in training. For these instances, the model can fit them well without concerns of MI attacks. An effective defense only needs to (possibly implicitly) identify instances that are vulnerable to MI attacks and avoids overfitting them. A major challenge is how to achieve such an effect in an efficient training process. Leveraging two distinct recent advancements in representation learning: counterfactually-invariant representations and subspace learning methods, we introduce a novel Membership-Invariant Subspace Training (MIST) method to defend against MI attacks. MIST avoids overfitting the vulnerable instances without significant impact on other instances. We have conducted extensive experimental studies, comparing MIST with various other state-of-the-art (SOTA) MI defenses against several SOTA MI attacks. We find that MIST outperforms other defenses while resulting in minimal reduction in testing accuracy. </p><p dir="ltr"><br></p>

Thesis (Ph.D.) -- University of Adelaide, School of Social Sciences, Discipline of Geographical and Environmental Studies, 2008. / "December 2007" Bibliography: leaves 247-253. Also available in print form.

Honors Project--Smith College, Northampton, Mass., 2009. / Includes bibliographical references.

Thesis (M.B.A.)--University of Hong Kong, 1998. / Includes bibliographical references (leaf 69-71).

Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 126 pages. Includes vita. Includes bibliographical references.

Thesis--University of Georgia. / Photocopy. Ann Arbor, Mich., University Microfilms, 1971. -- 21 cm. Includes bibliographical references (leaves 50-56).

Thesis (M.S.)--Missouri University of Science and Technology, 2010. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).

Over the last couple of years, the world has witnessed an intensifying competition over 5G networks, triggered to a large extent but not exclusively by the geopolitical rivalry between the United States and China. To the backdrop of allegations that the Chinese government could force Chinese telecom company Huawei and ZTE to spy, sabotage or take other action's on Beijing's behalf, Washington ordered prompt restrictions on products from Huawei and pressured its allies to do the same. From a European perspective, Sweden stood out early as a country with a strong stance on 5G security by outright banning Chinese telecom providers Huawei and ZTE from taking part in Sweden's 5G frequency auction. This thesis seeks to understand how the securitising process of Sweden's 5G networks was initiated and evolved, through a comparative case study of the four main securitising actors' official discourses. Derived from previous studies on cybersecurity and securitisation, this thesis constructed an analytical framework tailored for the securitisation of 5G networks. The thesis is carried out as an idea analysis, looking for articulated threats pertaining to three distinct threat dimensions: 'network security', 'data and information protection' and 'China's assertiveness'. The analysis showed that all four...