Masters of Science / With the development of information and network technologies, Electronic Health Records (EHRs) management system has gained wide spread application in managing medical records. One of the major challenges of EHRs is the independent nature of medical institutions. This non-collaborative nature puts a significant barrier between patients, doctors, medical researchers and medical data. Moreover, unlike the unique and strong anti-tampering nature of traditional paper-based records, electronic health records stored in centralization database are vulnerable to risks from network attacks, forgery and tampering. In view of the data sharing difficulties and information security problems commonly found in existing EHRs, this dissertation designs and develops a credible Blockchain-based electronic health records (CB-EHRs) management system. To improve security, the proposed system combines digital signature (using MD5 and RSA) with Role-Based Access Control (RBAC). The advantages of these are strong anti-tampering, high stability, high security, low cost, and easy implementation. To test the efficacy of the system, implementation was done using Java web programming technology. Tests were carried out to determine the efficiency of the Delegated Byzantine Fault Tolerance (dBFT) consensus algorithm, functionality of the RBAC mechanism and the various system modules. Results obtained show that the system can manage and share EHRs safely and effectively. The expectation of the author is that the output of this research would foster the development and adaptation of EHRs management system.

<p>In this thesis, we address two applications of threshold cryptography — designing information escrows and key-distribution in cryptocurrency systems. We design escrow mechanisms in two-party and multi-party scenarios such that any unauthorized revelation of<br> data results in the loss of cryptocurrency by the dishonest party. Later, we discuss user mental models in adopting cryptocurrency wallets and propose a protocol to efficiently provide cryptographic keys to the users in large-user systems. An information escrow refers to users storing their data at a custodian such that it can be revealed later. In the case of unauthorized leakage of this data by the custodian (receiver of data), taking legal actions is expensive, time consuming and also difficult owing to difficulty in establishing the responsibility. We address this by automatically penalizing the custodian through the loss of cryptocurrency in case of leakage. Initially, we consider a two party scenario where a sender forwards multimedia data to a receiver; we propose the Pepal protocol<br> where any total or partial leakage of data penalizes the receiver. To avoid single point of failure at the receiver in a two-party system, we extend the protocol to a multi-party system where a group of agents offer the escrow as a service. However, this introduces a collusion scenario among the rational agents leading to premature and undetectable unlocking of the data. Addressing this, we propose a collusion-deterrent escrow (CDE) protocol where any collusion among the agents is penalized. We show that the provably secure protocol deters collusion in game-theoretic terms by dis-incentivising it among the rational agents. In the second part of this work, we investigate the mental models of cryptocurrency wallet users in choosing single-device or multi-device wallets along with their preferences. We investigate the user-preferred default (threshold) settings for the key distribution in the wallets. We then propose the D-KODE protocol, an efficient key-generation mechanism for<br> cryptocurrency systems where either the payee or payer may not have the cryptographic setup but wish to transact. The protocol utilizes a practical black-box secret sharing scheme along with a distributed almost key-homomorphic PRF to achieve the threshold key distribution.</p>

We propose the use of depth-information to protect privacy in person-aware visual systems while preserving important foreground subjects and scene structures. We aim to preserve the identity of foreground subjects while hiding superfluous details in the background that may contain sensitive information. We achieve this goal by using depth information and relevant human detection mechanisms provided by the Kinect sensor. In particular, for an input color and depth image pair, we first create a sensitivity map which favors background regions (where privacy should be preserved) and low depth-gradient pixels (which often relates a lot to scene structure but little to identity). We then combine this per-pixel sensitivity map with an inhomogeneous image obscuration process for privacy protection. We tested the proposed method using data involving different scenarios including various illumination conditions, various number of subjects, different context, etc. The experiments demonstrate the quality of preserving the identity of humans and edges obtained from the depth information while obscuring privacy intrusive information in the background. / Computer and Information Science

The sharing of data has become integral to advancing scientific research, but it introduces challenges related to safeguarding personally identifiable information (PII). This thesis addresses the specific problem of sharing drivers' face videos for transportation research while ensuring privacy protection. To tackle this issue, we leverage recent advancements in generative adversarial networks (GANs) and demonstrate their effectiveness in deidentifying individuals by swapping their faces with those of others. Extensive experimentation is conducted using a large-scale dataset from ORNL, enabling the quantification of errors associated with head movements, mouth movements, eye movements, and other human factors cues. Additionally, qualitative analysis using metrics such as PERCLOS (Percentage of Eye Closure) and human evaluators provide valuable insights into the quality and fidelity of the deidentified videos. To enhance privacy preservation, we propose the utilization of synthetic faces as substitutes for real faces. Moreover, we introduce practical guidelines, including the establishment of thresholds and spot checking, to incorporate human-in-the-loop validation, thereby improving the accuracy and reliability of the deidentification process. In addition to this, this thesis also presents mitigation strategies to effectively handle reidentification risks. By considering the potential exploitation of soft biometric identifiers or non-biometric cues, we highlight the importance of implementing comprehensive measures such as robust data user licenses and privacy protection protocols. / Master of Science / With the increasing availability of large-scale datasets in transportation engineering, ensuring the privacy and confidentiality of sensitive information has become a paramount concern. One specific area of concern is the protection of drivers' facial data captured by the National Driving Simulator (NDS) during research studies. The potential risks associated with the misuse or unauthorized access to such data necessitate the development of robust deidentification techniques. In this thesis, we propose a GAN-based framework for the deidentification of drivers' face videos while preserving important facial attribute information. The effectiveness of the proposed framework is evaluated through comprehensive experiments, considering various metrics related to human factors. The results demonstrate the capability of the framework to successfully deidentify face videos, enabling the safe sharing and analysis of valuable transportation research data. This research contributes to the field of transportation engineering by addressing the critical need for privacy protection while promoting data sharing and advancing human factors research.

The purpose of this thesis is to elucidate what online profiling is, what happens with users' or customers' personal data during this process, how these activities interfere with the individuals' right to privacy, what the legal regulation in this field is, whether the privacy interests of individuals are sufficiently protected and if not, how the situation might be improved. The thesis starts with description of todays' business practices that are based on collecting data about customers, analyzing it and creating profiles suggesting the most profitable behaviour of businesses towards customers. It is followed by explanation of the technological tools enabling data collection and the method of data mining that is the key enabler of creating profiles. The text continues with description of risks of profiling in relation to privacy, i.e. the issues of discrimination, de-individualisation, restriction of individual autonomy, information asymmetries and possible misuse of profiles. The notion of right to privacy is explained and other interests that have to be balanced with privacy are mentioned as well. After that follows a critical description of the current legal framework in the European Union. It consists of Data Protection Directive, ePrivacy Directive and since 2018 of the General Data...

<div>The Internet has substantially embraced mobility since last decade. Cellular data network carries majority of Internet mobile access traffic and become the de facto solution of accessing Internet in mobile fashion, while many clean-slate Internet mobility solutions were proposed but none of them has been largely deployed. Internet mobile users increasingly concern more about their privacy as both researches and real-world incidents show leaking of communication and location privacy could lead to serious consequences. Just the communication itself between mobile user and their peer users or websites could leak considerable privacy of mobile user, such as location history, to other parties. Additionally, comparing to ordinary Internet access, connecting through cellular network yet provides equivalent connection stability or longevity.</div><div><br></div><div>In this research we proposed a novelty paradigm that leverages concurrent far-side proxies to maximize network location privacy protection and minimize interruption and performance penalty brought by mobility. To avoid the deployment feasibility hurdle we also investigated the root causes impeding popularity of existing Internet mobility proposals and proposed guidelines on how to create an economical feasible solution for this goal. Based on these findings we designed a mobility support system offered as a value-added service by mobility service providers and built on elastic infrastructure that leverages various cloud aided designs, to satisfy economic feasibility and explore the architectural trade-offs among service QoS, economic viability, security and privacy. </div>

This thesis examines how English law has, and has not, balanced celebrities’ legal expectations of informational and seclusional privacy against the press and media’s rights to inform and publish. Much of the litigation that developed the English laws of privacy has been celebrity-generated by those with the financial resources to seek out and utilize privacy regimes and remedies in ways not immediately available to ordinary members of the public. The media, generally, has had the resources to present the relevant counter-arguments. Privacy protection was initially afforded to celebrities by breach of confidence and copyright. While public interest and “fair dealing” defences developed within English law, there was no underlying or consistent practical element in legislative or judicial thinking to promote a balance between the competing interests of protection and interference. That practical element, the concept of proportionality, developed in the Convention case-law of the ECtHR in Strasbourg during the 1950s. It was not until the Human Rights Act 1998 (HRA) that English legislators and the UK judicial system began to reflect and apply its consequences. Arriving at proportionate results and decisions – particularly in the realms of privacy - requires both the engagement of the rights that are sought to be maintained as well as a careful balancing exercise of these rights both internally and vis-à-vis each other. Because celebrities, with their Article 8 concerns, and the media, with Article 10 arguments, seek for their causes to prevail, the ways in which legislation and litigation now resolves matters is by the “ultimate balancing test” of proportionality. Proportionality is the measure within this thesis that is constant from chapter to chapter, highlighting, respectively, where the application of proportionality and balance might have produced different results as regimes developed historically and where new developments were needed to accommodate its requirements when it was apparently absent.

<p>Following the trend of online data sharing and publishing, researchers raise their concerns about the privacy problem. Online Social Networks (OSNs), for example, often contain sensitive information about individuals. Therefore, anonymizing network data before releasing it becomes an important issue. This dissertation studies the privacy preservation problem from the perspectives of both attackers and defenders. </p> <p><br></p> <p>To defenders, preserving the private information while keeping the utility of the published OSN is essential in data anonymization. At one extreme, the final data equals the original one, which contains all the useful information but has no privacy protection. At the other extreme, the final data is random, which has the best privacy protection but is useless to the third parties. Hence, the defenders aim to explore multiple potential methods to strike a desirable tradeoff between privacy and utility in the published data. This dissertation draws on the very fundamental problem, the definition of utility and privacy. It draws on the design of the privacy criterion, the graph abstraction model, the utility method, and the anonymization method to further address the balance between utility and privacy. </p> <p><br></p> <p>To attackers, extracting meaningful information from the collected data is essential in data de-anonymization. De-anonymization mechanisms utilize the similarities between attackers’ prior knowledge and published data to catch the targets. This dissertation focuses on the problems that the published data is periodic, anonymized, and does not cover the target persons. There are two thrusts in studying the de-anonymization attacks: the design of seed mapping method and the innovation of generating-based attack method. To conclude, this dissertation studies the online data privacy problem from both defenders’ and attackers’ point of view and introduces privacy and utility enhancement mechanisms in different novel angles.</p>

In this thesis, we propose a new design of privacy and session control for improving a collaborative molecular modeling CVR system AMMP-VIS [1]. The design mainly addresses the issue of competing user interests and privacy protection coordination. Based on our investigation of AMMP-VIS, we propose a four-level access control structure for collaborative sessions and dynamic action priority specification for manipulations on shared molecular models. Our design allows a single user to participate in multiple simultaneous sessions. Moreover, a messaging system with text chatting and system broadcasting functionality is included. A 2D user interface [2] for easy command invocation is developed in Python. Two other key aspects of system implementation, the collaboration Central deployment and the 2D GUI for control are also discussed. Finally, we describe our system evaluation plan which is based on an improved cognitive walkthrough and heuristic evaluation as well as statistical usage data.

The Study of Customer Personal Data Protection In this customer-driven era, corporations and government agencies face the challenges from customers. If government and corporations can utilize the power of computers to manage the huge amount of personal data they have collected by storing and editing, data mining and customer relationship management can be put to use on services, customer cares, and marketing. This will increase the efficiency of government agencies and stimulate the development of economy. The government, corporations and the people all will be benefited from this move. However, while the organizations make large investments in the security of their computer systems to avoid the invasion of virus and hackers, the abuse and breach caused by the employees, contractors, and other legal users can compromise all the preventive measures. This study investigates the performance of customer personal data privacy protection. While discussing the regulations such as computer processing of personal data acts and Telecommunications Acts, the theory on which this study is based is Self-Regulation Mechanism. The Self-Regulation Mechanism can be applied to the self-monitoring, self-esteem, information ethics, and self-efficacy of the users who have access to the customer personal data. It can also be applied to the management of the customer personal data privacy at the organization level. This study gathered 432 valid surveys from the customer personal data users who are the customer service staffs in the telecommunication industry. With path analysis methodology, this study explores the interactions among the management of organization, personal privacy protection self-efficacy, and information ethics. With information ethics and self-efficacy as the intervening variable between the management of organization and protection performance, this study is set to clarify the level of impacts that these three items have over the performance of customer information privacy protection. Through the model validation, the customer personal data protection self-regulation mechanism proposed in this study demonstrates suitability and the management of organization also shows positive, direct and noticeable impacts. However, the effects of information ethics on privacy protection self-efficacy and those of self-efficacy on the performance of privacy protection are not obvious. Therefore, the organization should strengthen the information ethics of its employees and improve self-efficacy. Also, they should bring up feasible and solid suggestions, hoping to improve the customer personal privacy protection performance of the organization and its members. By doing the customers will have confidence in the organization. Winning the trust and satisfaction from the customers will promote the organization image and even bring in more business opportunities, a good thing for running a long term business.