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

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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

Service-oriented architectures (SOA), and in particular Web services, have quickly become a popular paradigm to develop distributed applications. Nowadays, more and more organizations shift their core business to the Web services platform within which various interactions between the autonomous services occur. One of the widely accepted standards in the Web services platform is Business Process Execution Lan- guage for Web Services (BPEL4WS, or BPEL for short). BPEL defines a language to integrate Web services by creating composite Web services in the form of business processes following the service orchestration paradigm, and it enables organizations to focus on core competence and mission-critical operations while outsource every- thing else to reduce costs and time to market. However BPEL is deficient in privacy issues. The facts are: (1) service requestors?? personal information is fundamental to enable business processes (e.g., the mortgage approval business process); (2) privacy concerns have become one of the most important issues in Information Technology and has received increasing at- tention from organizations, consumers and legislators; (3) most organizations have recognized that dealing correctly and honestly with customers?? privacy concerns can have beneficial returns for their businesses, not only in terms of being compliant with laws and regulations but also in terms of reputation and potential business op- portunities. If not addressed properly, privacy concerns may become an impediment to the widespread adoption of BPEL. Privacy issues have many aspects, the privacy concerns of potential service re- questor (i.e., client) and the privacy concerns of service provider (i.e., organization) are two of them. Service requestor specifies his/her privacy concerns as privacy preference, while service provider defines and publishes its privacy policy to specify its privacy promises. Before requestor accesses certain service, he/she likes to know whether the service provider will respect his/her privacy preference. Otherwise, the requestor may seek the desired service from somewhere else. On the other hand,even though most organizations publish their privacy promises, it will be more convincing if customers are assured that such privacy promises are actually kept within the organizations. In this thesis, we propose a privacy enforcement framework for business processes. In particular, we focus on those that are automated using BPEL. The framework consists of two parts. One focuses on the service requestors?? perspective of privacy, the other concentrates on the privacy concerns of the business process owner (i.e., the service provider). More specifically, the first part of the framework is based on description logic, and allows to represent privacy concepts and perform some rea- soning about these concepts. The reasoning engine will check requestor??s privacy preference against the service provider??s published privacy promises before the re- questor accesses the desired service. The second part of the framework facilitates the service provider to enforce its privacy policy within all its business processes throughout the life cycle of personal data. The privacy enforcement can be achieved step by step: privacy inspection, privacy verification and privacy obligation man- agement. The first step, privacy inspection, aims to identify which activity needs the involvement of what personal data. The second step, privacy verification, is to verify the correctness of designed BPEL business processes in terms of privacy. The third step is to enforce the privacy by managing the fulfillment of the obligation during the execution of business process. The privacy enforcement framework presented in the thesis has been implemented. The first part of the framework is implemented in the Privacy Match Engine prototype. For the second part of the framework, as different parts of the privacy policy need to be enforced at different stages of the life cycle of business processes, the implementation consists of a privacy verification tool and a privacy obligation management system.

In recent years, the Smart Grid has grown to be the solution for future electrical energy that promises to avoid blackouts as well as to be energy efficient, environmentally and customer-friendly. In Smart Grid, the customer-friendly applications are a key element that provides the feature for recognizing the active expenditure of current energy via an Advanced Metering Infrastructure (AMI) subsystem. In fact, the smart meter, as a major part of AMI that is installed in residences, which provides more details about a consumer‟s usage. The smart meter measures hour-by-hour usage of a house, and then instantly transmits the record to the utility via two-way communications, unlike the previous electrical system that collects all usage monthly. However, the live measurement of the usage creates a potential privacy leak since each electrical usage records the behaviour of consumers in the home. Therefore, any communication channel between customers and utility should have some sort of confidentiality which protects consumer privacy. In reality, smart meters are generally located in an insecure area of the house (outside), therefore anyone can potentially tamper with the device, noting the fact that it is low-end device. As a result, there is a great possibility of compromising the smart meter, resulting in disclosure of consumer usage. Actually, the nature of a smart meter, and the cost constraints, create a challenge to secure the network. Therefore, the dual motivating problems are the protection of consumer privacy as well as achieving cost efficiency. In this research, we propose a new secure and compromise resilient architecture that continues two major components: a smart meters compromise attack detection scheme and a secure usage reporting protocol. Firstly, the smart meters compromise attack detection scheme improves the security of the smart meter, preventing an adversary from compromising the smart meter. Secondly, the secure usage reporting protocol improves the security of communication between the smart meter and the utility, preventing an adversary from identifying each household's usage reported by smart meters. / UOIT