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.

In health services, it is a necessity to keep the records of the patients. Although paper-based records are commonly used for this aim, they are not as convenient as computerized records. Therefore, many of the health facilities have recently started keeping patients&rsquo / health records in electronic databases. However, new questions about confidentiality and privacy of these records were raised with this new system.This study aims to investigate the opinions and attitudes of the people who use the health services of Turkey about the privacy and confidentiality of health information in electronic environment. In the survey, there are 596 participants from 64 different cities in six geographical regions of Turkey. The findings show that people feel comfortable about computer usage in health-care but they are concerned about the privacy and confidentiality of their information and also they are not sure if their medical information is safe and secure now. Moreover, they are mostly unaware about current regulations related to information privacy in Turkey. The study also shows that people trust in their doctors, health researchers in universities, pharmacist, nurses and other hospital staff but do not trust in insurance companies, government, private sector health researchers, information technology specialists and government health researchers for the privacy of their medical records.

Although IS studies have begun to recognize the role of emotion in decision making, the research in this area is still in its infancy. The exploration of IS decision making phenomena through the lens of regret can offer rich implications to both research and practice. The presence of regret, for instance, can explain how and why IS decision makers choose a certain option. Motivated by the gap in the literature, the three papers in this dissertation investigate the role of regret in decision making in IS contexts. Specifically, the three projects investigate the following: IT real options decision in the context of RFID investment in libraries, whistle-blowing decision in the context of violations of heath information privacy, and process documentation decision in the context of investment in process improvement initiatives in an IT project. The contributions and implications of the three studies are presented further.

Communication networks are an integral part of the Internet of Things (IoT) era. They enable endless opportunities for connectivity in a wide range of applications, leading to advances in efficiency of day-to-day life. While creating opportunities, they also incur several new challenges. In general, we wish to design a system that performs optimally well in all aspects. However, there usually exist competing objectives which lead to tradeoffs. In this thesis, driven by several applications, new features and objectives are included into the system model, making it closer to reality and needs. The results presented in this thesis aim at providing insight into the fundamental tradeoff of the system performance which can serve as a guideline for the optimal design of real-world communication systems. The thesis is divided into two parts. The first part considers the aspect of signal reconstruction requirement as a new objective in the source and channel coding problems. In this part, we consider the framework where the quality and/or availability of the side information can be influenced by a cost-constrained action sequence. In the source coding problem, we impose a constraint on the reconstruction sequence at the receiver that it should be reproduced at the sender, and characterize the fundamental tradeoff in the form of the rate-distortion-cost region, revealing the optimal relation between compression rate, distortion, and action cost. The channel coding counterpart is then studied where a reconstruction constraint is imposed on the channel input sequence such that it should be reconstructed at the receiver. An extension to the multi-stage channel coding problem is also considered where inner and outer bounds to the capacity region are given. The result on the channel capacity reveals interesting consequence of imposing an additional reconstruction requirement on the system model which has a causal processing structure. In the second part, we consider the aspect of information security and privacy in lossy source coding problems. The sender wishes to compress the source sequence in order to satisfy a distortion criterion at the receiver, while revealing only limited knowledge about the source to an unintended user. We consider three different aspects of information privacy. First, we consider privacy of the source sequence against the eavesdropper in the problem of source coding with action-dependent side information. Next, we study privacy of the source sequence due to the presence of a public helper in distributed lossy source coding problems. The public helper is assumed to be either a user who provides side information over a public link which can be eavesdropped, or a legitimate user in the network who helps to relay information to the receiver, but may not ignore the information that is not intended for it. Lastly, we take on a new perspective of information privacy in the source coding problem. That is, instead of protecting the source sequence, we are interested in the privacy of the reconstruction sequence with respect to a user in the system. For above settings, we provide the complete characterization of the rate-distortion(-cost)-leakage/equivocation region or corresponding inner and outer bounds for discrete memoryless systems. / <p>QC 20140514</p>

Individuals in the UAE and Abu Dhabi Emirate, in particular, have become increasingly concerned about their private information. This is mainly due to the use of technology, which makes accessing, transmitting and editing personal information faster and easier. Besides the use of technology, and the awareness and understanding of the privacy of expatriates, working in Abu Dhabi Emirate has had an impact on UAE citizens in terms of their rights to privacy. There is a need for organisations to comply with international bodies in protecting individuals rights to privacy and to increase the exploration of culturally sensitive information in the media. These issues have all led to the importance of and need to explore and identify Abu Dhabi Emirate employees perceptions, and the factors influencing their behaviour, towards privacy. The aim of this research was to investigate and analyse factors influencing employees information privacy behaviour and employees perceptions, awareness and behaviour on the handling of private information in the Abu Dhabi Emirate public sector, ADEC, as well as to provide practical recommendations to improve the privacy. The research methods used in this project are based on a mixed-method approach comprising both quantitative and qualitative strategies. Qualitative data collection in this research included face-to-face interviews and focus groups with Abu Dhabi Education Council. Quantitative surveys for all the Abu Dhabi Education Council were also utilised. The research identified the types of information that were considered private and defined privacy in the context of UAE culture. The main factors influencing privacy in Abu Dhabi Emirate employees were identified and analysed such as national culture, organisation culture and perceived benefits as examples. Following this, practical recommendations for changes to promote and enhance privacy in Abu Dhabi Emirate were offered. A model has been developed and designed based on the factors influencing individual information behaviour regarding private information handling, interrelated and influenced. This is essential to provide a practical model capable of acting as a guideline to help organisations, decision makers, and strategic planners in the Abu Dhabi Emirate public sector decide how best to approach privacy policy.

Information privacy is a major concern for consumers adopting emerging technologies dependent on location-based services. This study sought to determine whether a relationship exists among factors of personalization, locatability, perceived playfulness, privacy concern and behavioral intention to disclose personal information for individuals using location-based, geosocial networking applications. Questionnaire responses from undergraduate students at a 4-year university provide insight into these relationships. Multiple regression results indicated that there was a statistically significant relationship between the four significant predictor variables and the dependent variable. Analysis of beta weights, structure coefficients, and commonality analysis shed light on the variance attributable to the predictor variables of the study. Findings provide understanding of the specific factors examined in the study and have implications for consumers, businesses, application designers, and policymakers. The results from this study contribute to an understanding of technology acceptance theory and offer insight into competing beliefs that may affect an individual’s behavioral intention to disclose personal information. Knowledge gained form the study may be useful for overcoming challenges related to consumer adoption of location-based services that require disclosure of personal information.

Definition of privacy has changed by the changes and improvements in information and surveillance technologies. These changes and improvement need new legal decisions for new kinds of privacy invasions. This study explores the scope of privacy right, particularly when a technological surveillance has occurred by law enforcement agencies. It focuses in particular on increasing law enforcements' surveillance technologies and devices that have the potential to impact citizens' information privacy. These increasing changes in surveillance technologies have important implications both for law enforcements and citizens. This study also discusses increasing law enforcement surveillance for the public's security, changes of the laws that allow law enforcements to use new surveillance powers as a war on terrorism, and the citizens concerns of information privacy. A particular attention is given to the recent public opinion surveys which show citizens' increasing privacy concerns. Finally, a set of recommendations to figure out security-privacy debate and reduce the privacy concerns of the citizens is offered.

<p>Collecting user data is crucial for advancing machine learning, social science, and government policies, but the privacy of the users whose data is being collected is a growing concern. {\em Differential Privacy (DP)} has emerged as the most standard notion for privacy protection with robust mathematical guarantees. Analyzing such massive amounts of data in a privacy-preserving manner motivates the need to study differentially-private algorithms that are also super-efficient.  </p> <p><br></p> <p>This thesis initiates a systematic study of differentially-private sublinear-time and sublinear-space algorithms. The contributions of this thesis are two-fold. First, we design some of the first differentially private sublinear algorithms for many fundamental problems. Second, we develop general DP techniques for designing differentially-private sublinear algorithms. </p> <p><br></p> <p>We give the first DP sublinear algorithm for clustering by generalizing a subsampling framework from the non-DP sublinear-time literature. We give the first DP sublinear algorithm for estimating the maximum matching size. Our DP sublinear algorithm for estimating the average degree of the graph achieves a better approximation than previous works. We give the first DP algorithm for releasing $L_2$-heavy hitters in the sliding window model and a pure $L_1$-heavy hitter algorithm in the same model, which improves upon previous works.  </p> <p><br></p> <p>We develop general techniques that address the challenges of designing sublinear DP algorithms. First, we introduce the concept of Coupled Global Sensitivity (CGS). Intuitively, the CGS of a randomized algorithm generalizes the classical  notion of global sensitivity of a function, by considering a coupling of the random coins of the algorithm when run on neighboring inputs. We show that one can achieve pure DP by adding Laplace noise proportional to the CGS of an algorithm. Second, we give a black box DP transformation for a specific class of approximation algorithms. We show that such algorithms can be made differentially private without sacrificing accuracy, as long as the function has small global sensitivity. In particular, this transformation gives rise to sublinear DP algorithms for many problems, including triangle counting, the weight of the minimum spanning tree, and norm estimation.</p>

<p>Mobile systems, such as smartphones, wearables (e.g., smartwatches, AR/VR headsets),<br> and IoT devices, have come a long way from being just a method of communication to<br> sophisticated sensing devices that monitor and control several aspects of our lives. These<br> devices have enabled several useful applications in a wide range of domains ranging from<br> healthcare and finance to energy and agriculture industries. While such advancement has<br> enabled applications in several aspects of human life, it has also made these devices an<br> interesting target for adversaries.<br> In this dissertation, I specifically focus on how the various sensors on mobile devices can<br> be exploited by adversaries to violate users’ privacy and present methods to use sensors<br> to improve the security of these devices. My thesis posits that multi-modal sensing can be<br> leveraged to enhance the security and privacy of mobile systems.<br> In this, first, I describe my work that demonstrates that human interaction with mobile de-<br> vices and their accessories (e.g., stylus pencils) generates identifiable patterns in permissionless<br> mobile sensors’ data, which reveal sensitive information about users. Specifically, I developed<br> S3 to show how embedded magnets in stylus pencils impact the mobile magnetometer sensor<br> and can be exploited to infer a users incredibly private handwriting. Then, I designed LocIn<br> to infer a users indoor semantic location from 3D spatial data collected by mixed reality<br> devices through LiDAR and depth sensors. These works highlight new privacy issues due to<br> advanced sensors on emerging commodity devices.<br> Second, I present my work that characterizes the threats against smartphone authentication<br> and IoT device pairing and proposes usable and secure methods to protect against these threats.<br> I developed two systems, FaceRevelio and IoTCupid, to enable reliable and secure user and<br> device authentication, respectively, to protect users’ private information (e.g., contacts,<br> messages, credit card details) on commodity mobile and allow secure communication between<br> IoT devices. These works enable usable authentication on diverse mobile and IoT devices<br> and eliminate the dependency on sophisticated hardware for user-friendly authentication.</p>

<p>This dissertation explores the world of non-intrusive wireless sensing for diet and fitness activity monitoring, in addition to assessing security risks in human activity recognition (HAR). It delves into the use of WiFi and millimeter wave (mmWave) signals for monitoring eating behaviors, discerning intricate eating activities, and observing fitness movements. The proposed systems harness variations in wireless signal propagation to record human behavior while providing exhaustive details on dietary and exercise habits. Significant contributions encompass unsupervised learning methodologies for detecting dietary and fitness activities, implementing soft-decision and deep neural networks for assorted activity recognition, constructing tiny motion mechanisms for subtle mouth muscle movement recovery, employing space-time-velocity features for multi-person tracking, as well as utilizing generative adversarial networks and domain adaptation structures to enable less cumbersome training efforts and cross-domain deployments. A series of comprehensive tests validate the efficacy and precision of the proposed non-intrusive wireless sensing systems. Additionally, the dissertation probes the security vulnerabilities in mmWave-based HAR systems and puts forth various sophisticated adversarial attacks - targeted, untargeted, universal, and black-box. It designs adversarial perturbations aiming to deceive the HAR models whilst striving to minimize detectability. The research offers powerful insights into issues and efficient solutions relative to non-intrusive sensing tasks and security challenges linked with wireless sensing technologies.</p>