No description available.

<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>

Open access to health data can bring enormous social and economical benefits. However, such access can also lead to privacy breaches, which may result in discrimination in insurance and employment markets. Privacy is a subjective and contextual concept, thus it should be interpreted from both systemic and information perspectives to clearly understand potential breaches and consequences. This dissertation investigates three popular use cases of healthcare data: specifically, 1) synthetic data publication, 2) aggregate data utilization, and 3) privacy-aware API implementation. For each case, we develop statistical models that improve the privacy-utility Pareto frontier by leveraging a variety of machine learning techniques such as information theoretic privacy measures, Bayesian graphical models, non-parametric modeling, and low-rank factorization techniques. It shows that much utility can be extracted from health records while maintaining strong privacy guarantees and protection of sensitive health information. / text

No description available.

No description available.

Swinging is a lifestyle choice where members of a couple seek out other couples or sometimes singles, with whom to engage in sexual activity. Swinging is a lifestyle associated with the 1960s and 1970s, but Americans still engage in swinging activities today. Because of stigmas associated with this practice, swinging couples often keep their lifestyle concealed from family and friends. These couples have a unique lifestyle that requires strong communication and boundary management styles. Scholars use communication privacy management theory to examine how individuals or couples disclose private information and how this private information is then co-owned by both parties. The purpose of this study was to understand whom swinging couples disclose their lifestyle to, and what risks the couple experienced from the disclosures. The swingers disclosed to friends in most cases and were concerned about risks of stigma, privacy, and relationship termination. In this exploratory study I showed that swingers’ privacy management seems to align with the components of CPM in concealing or revealing their lifestyle to others. However the findings also indicate that swingers utilize self-disclosure for recruitment into the lifestyle, and that the disclosures seem to be more spontaneous then strategic. Future research should look further into the privacy management of swingers, as well as other ways in which they manage their stigmatized identities.

The Internet and the growth of Information Technology (IT) and their enhanced capabilities to collect personal information have given rise to many privacy issues. Unauthorized access of personal information may result in identity theft, stalking, harassment, and other invasions of privacy. Information privacy concerns are impediments to broad-scale adoption of the Internet for purchasing decisions. Computer self-efficacy has been shown to be an effective predictor of behavioral intention and a critical determinant of intention to use Information Technology. This study investigated the relationship between an individual's computer self-efficacy and information privacy concerns; and also examined the differences among different age groups and between genders regarding information privacy concerns and their relationships with computer self-efficacy. A paper-based survey was designed to empirically assess computer self-efficacy and information privacy concerns. The survey was developed by combining existing validated scales for computer self-efficacy and information privacy concerns. The target population of this study was the residents of New Jersey, U.S.A. The assessment was done by using the mall-intercept approach in which individuals were asked to fill out the survey. The sample size for this study was 400 students, professionals, and mature adults. The Shapiro-Wilk test was used for testing data normality and the Spearman rank-order test was used for correlation analyses. MANOVA test was used for comparing mean values of computer self-efficacy and information privacy concerns between genders and among age groups. The results showed that the correlation between computer self-efficacy and information privacy concerns was significant and positive; and there were differences between genders and among age groups regarding information privacy concerns and their relationships with computer self-efficacy. This study contributed to the body of knowledge about the relationships among antecedents and consequences of information privacy concerns and computer self-efficacy. The findings of this study can help corporations to improve e-commerce by targeting privacy policy-making efforts to address the explicit areas of consumer privacy concerns. The results of this study can also help IT practitioners to develop privacy protection tools and processes to address specific consumer privacy concerns.

In many prevalent application domains, such as business to business network, social networks, and sensor networks, graphs serve as a powerful model to capture the complex relationships inside. These graphs are of significant importance in various domains such as marketing, psychology, and system design. The management and analysis of these graphs is a recurring research theme. The increasing scale of data poses a new challenge on graph analysis tasks. Meanwhile, the revealed edge uncertainty in the released graph raises new privacy concerns for the individuals involved. In this dissertation, we first study how to design an efficient distributed triangle listing algorithms for web-scale graphs with MapReduce. This is a challenging task since triangle listing requires accessing the neighbors of the neighbor of a vertex, which may appear arbitrarily in different graph partitions (poor locality in data access). We present the “Bermuda” method that effectively reduces the size of the intermediate data via redundancy elimination and sharing of messages whenever possible. Bermuda encompasses two general optimization principles that fully utilize the locality and re-use distance of local pivot message. Leveraging these two principles, Bermuda not only speeds up the triangle listing computations by factors up to 10 times but also scales up to larger datasets. Second, we focus on designing anonymization approach to resisting de-anonymization with little utility loss over uncertain graphs. In uncertain graphs, the adversary can also take advantage of the additional information in the released uncertain graph, such as the uncertainty of edge existence, to re-identify the graph nodes. In this research, we first show the conventional graph anonymization techniques either fails to guarantee anonymity or deteriorates utility over uncertain graphs. To this end, we devise a novel and efficient framework Chameleon that seamlessly integrates uncertainty. First, a proper utility evaluation model for uncertain graphs is proposed. It focuses on the changes on uncertain graph reliability features, but not purely on the amount of injected noise. Second, an efficient algorithm is designed to anonymize a given uncertain graph with relatively small utility loss as empowered by reliability-oriented edge selection and anonymity-oriented edge perturbing. Experiments confirm that at the same level of anonymity, Chameleon provides higher utility than the adaptive version of deterministic graph anonymization methods. Lastly, we consider resisting more complex re-identification risks and propose a simple-yet-effective Galaxy framework for anonymizing uncertain graphs by strategically injecting edge uncertainty based on nodes’ role. In particular, the edge modifications are bounded by the derived anonymous probabilistic degree sequence. Experiments show our method effectively generates anonymized uncertain graphs with high utility.

"On the modern web, many sites have third party content, be it through maps, embedded objects, ads, or through other types. Users pay little attention to the source of this content since it is such a common occurrence. Unfortunately, this content can be an avenue for third parties to discover private information about the user. Previous work has found these types of leaks in social networking sites. By logging headers during the usage of 120 sites across 12 major categories, we were able to find leakage of a user’s private information occurring on many other types of popular web sites. We found leakage on 75% of the sites we looked at and at least one instance in each of the categories. Based on the leaks we found, we propose a classification of the types of leakage that can occur via the HTTP header and use this system to analyze our results."

Thesis (M.S.)--Boston University