Information Privacy Protection Research Articles

Estimation method for distance cost to access medical services: Policy and patient privacy implications in Taiwan.

Indicators of healthcare access with high reliability, validity, timeliness, and easy application can aid in an understanding of the supply and demand of a region's medical resources and assist governments in allocating resources more effectively. However, a key concern when developing indicators is the protection of private information, such as patients' residential addresses. We develop an estimation method for distance cost using official public information, including a region's disease prevalence rates and population. The method accounts for patients' privacy and addresses limitations associated with using the National Health Insurance Database. This cross-sectional study conducts a secondary data analysis using SPSS and QGIS. The data were divided into a validation group and an index development group with the medical distance calculated for each group. Data for the validation group were sourced from the medical records of patients with diabetes (n = 108-164) and hypertension (n = 243-348) in Yuli documented by a medical center in 2017-2019, and the data for the novel index development group included diabetes and hypertension prevalence sourced from national official public data. The study compared the consistency of the two groups' medical treatment distances to verify the accuracy of the estimation method. The estimated distances for the index development group showed a high consistency (ICC > 0.9). Further, the index development group had an excellent R-square after adjusting for age (98.1%) and gender (92.7%). The proposed method to estimate healthcare on the basis of disease prevalence and population protects patient privacy and can be implemented by local governments. This study was approved by the Research Ethics Committee of the Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation (IRB109-239-B).

A balancing act: how risk mitigation strategies employed by users explain the privacy paradox on social media

ABSTRACT While there is a growing body of literature on information privacy suggesting different mechanisms of how people’s privacy concerns might be impacting their attitudes and behaviour when using social media, recent questionable data use practices by social media platforms and third parties call for a renewed validation of existing information privacy models. The objective of this research is to re-examine the variables predicting why people disclose information on social media. Building on previous work, this paper puts forward a comprehensive Privacy Concerns and Social Media Use Model (PC-SMU) and evaluates it in a specific cultural and legal environment (social media users from a single county, Canada). The study delves into the privacy paradox and shows that the benefits of using social media are the main driver of self-disclosure, and that self-disclosing behaviours are nuanced by the users’ information privacy protection strategies. We also find that higher levels of social media literacy, and concerns about organisational threats to a lesser extent, lead to higher levels of information privacy management, emphasizing the importance of educating users about how to use the different privacy and security features provided by social media platforms.

VRepChain: A Decentralized and Privacy-Preserving Reputation System for Social Internet of Vehicles Based on Blockchain

In the context of the social Internet of vehicles (SIoV), constructing reliable social relationships between dynamic and distributed entities is a challenging research problem. Rating-based reputation systems have been widely applied to assist human users in evaluating the honesty of target entities. However, the ratings in SIoV expose user privacy, including behavior, location, etc., which are required to be protected properly. Meanwhile, the blockchain technology with its distributed paradigm is potentially employed to protect information privacy. In this study, we propose the design of a blockchain-enabled reputation system named “VRepChain” for SIoV by especially considering the rating privacy issue. In our design, the ratings' privacy is strongly preserved in the processes of transmission and storage. The reputation of a vehicle is constructed based on the ratings with the agreement of the rating providers, ensuring the ratings are never abused by any other unauthorized entities during the usage process. Through experiments, the proposed system is demonstrated to improve the effectiveness of vehicles in terms of arriving at their destinations in a faster speed. Furthermore, the effectiveness of the constructed reputation model with untruthful ratings is extensively examined, showing its robustness and practicality in realistic applications.

Promotion strategy for online healthcare platform during the COVID-19 pandemic: Evidence from Spring Rain Doctor in China.

Online healthcare platform (OHP) is a new form of medical treatment that solves the problems of an unbalanced distribution of medical resources in China. Especially during the COVID-19 pandemic, OHP has greatly reduced the medical pressure of the hospital and the risk of cross-infection. Based on self-determination theory (SDT) (Ryan and Deci, 2000), privacy calculus theory (PCT) (Culnan, 1999) and perceived value theory (PVT) (Choi, 2004), this study uses evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the promotion of OHP. Moreover, we conduct numerical simulations with the help of program compilation. The results demonstrate that (1) both the qualification inspection of doctors and the investment in information protection influence doctors' participation in and patients' usage of OHP; (2) both the initial probabilities of doctor participation and patient usage influence the multi-game results; (3) the trend of doctors joining OHP is affected by registration cost, time cost, and reputation loss; and (4) the trend of patients using online healthcare is mainly decided by the cost. This study takes the Spring Rain Doctor as an example to verify the game results. To further popularize online medical treatment among patients, the platform should attach importance to the inspection of doctors and the protection of privacy information and strengthen its publicity in remote places.

Online taxi users' optimistic bias: China youths' digital travel and information privacy protection.

Digital travel platforms not only provided people with convenient travel but also raised a series of problems regarding information privacy protection. In order to analyze privacy protection behavior, this study surveyed 441 subjects aged 18-35 who utilized digital travel platforms based on a structural model of protective motivation theory. The results indicated that a perceived threat, self-efficacy, and response efficacy positively and significantly impacted youths' privacy concerns. Furthermore, privacy concerns were positively related to privacy protection behavior and were an intermediate variable between the relationships among perceived threat, self-efficacy, response efficacy, and privacy protection behavior. This study identified the moderating effect of youths' knowledge of platform privacy settings on the relationship between privacy concerns and protection behavior. In addition, the results confirmed that an optimistic bias did exist among talented youth with high privacy knowledge in terms of a practical level of privacy management. These unique findings represent the exceptional contributions and innovation points of this study.

A Survey of Deep Learning for Electronic Health Records

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research.

Privacy information protection algorithm of ultra dense network nodes based on edge computing

In order to solve the problems of high node loss rate, high time overhead and high risk of privacy disclosure in network node privacy information protection, an ultra dense network node privacy information protection algorithm based on edge computing is proposed. The weight update algorithm is used to detect the security vulnerabilities of ultra dense network nodes. According to the detection results, the characteristics of node vulnerabilities are obtained, the sensitive label information of node vulnerabilities is protected through the weighted graph, and the k-anonymity technology is used to anonymize the privacy information of ultra dense network nodes; Finally, edge computing is used to protect the privacy information of nodes. The experimental results show that the node loss rate of the proposed method is always less than 2%, the time overhead is small, and the risk coefficient of privacy disclosure is small.

A temporal–spatial charging coordination scheme incorporating probability of EV charging availability

The charging coordination of electric vehicle (EV) fleets in both temporal domain and spatial domain has attracted growing attention in recent years. Meanwhile, the uncertainties in EV arrival time and total available charging power from charging stations make the coordination problem highly dynamic and challenging. This paper develops a new temporal–spatial EV charging coordination scheme that jointly considers the above two major uncertainties. Firstly, EV charging scheduling (i.e., temporal coordination) is treated as a generalized Nash equilibrium game, in which each EV (including an upcoming EV) prefers to meet its own charging demand with minimized charging cost. The probability of EV charging availability is especially proposed to incorporate the charging demands of the upcoming EVs into the coordination scheme. In order to provide flexibility and private information protection, a distributed receding horizon optimization-based solution is developed, through which the Lagrange multipliers to reach the social equilibrium are determined via an iterative manner. The charging station selection is then recommended that minimizes the objective function over the entire optimization horizon. Finally, simulations under both small-scale and large-scale scenarios effectively demonstrate improved service quality of the EV charging, both in temporal and spatial domains, and avoidance of overload in charging stations. Results in a 150-EV scenario show that, averagely, the proposed method reduces battery SoC mismatch by 43% and increases degree of consistency by 5.9%.

Hierarchical Flexible Operation Approach on a VSC-MTDC Interconnected Hybrid Grid With a High Share of Renewable Power

Voltage source converter (VSC) based multi -terminal HVDC (VSC-MTDC) systems have emerged as flexible collector systems for multi-area interconnected AC/DC hybrid grids with high share of volatile renewables energy. However, it remains an open question as how to exploit the flexible and cooperative regulation of multiple VSC stations and enhance secure operation on the premise of information privacy protection of multiple independent dispatch areas. This paper proposed an Analytical Target Cascading (ATC)-based three-layer decentralized optimization approach on flexible operation of VSC-MTDC hybrid systems. The low layer is a chance-constrained stochastic optimal power flow (SOPF) problem for AC grids. To guard against the secure operation risk by a VSC's random unipolar atresia fault, a modified loss of load probability index (MLOLP) is proposed to improve the day-ahead generation-reserve scheme. The middle layer and the high layer are a multiperiod OPF problem for VSC stations and the MTDC grid, respectively, where a detailed power regulation model is built to fully exert the flexible cooperation of multiple VSC stations. Two case systems are tested to verify the effectiveness and superiority of the proposed flexible operation hierarchy.

Behind open doors: Patient privacy and the impact of design in primary health care, a qualitative study in Indonesia.

The importance and attention to patient privacy in recent decades have been directed mostly toward medical data protection in electronic means. Hence, other aspects of patients' privacy were overlooked, particularly in the primary health care (PHC) level. In the attempt of many countries, including Indonesia, to strive toward universal healthcare provision, a strong and accessible PHC is essential. This situation may create a tension in privacy provision where patients who need to disclose secrets may opt for other facilities, such as hospitals. This study aimed to describe and discuss patients' and doctors' perspectives and experiences about privacy in PHC in Indonesia, particularly since the universal coverage started. We used in-depth interviews and observations to gather information. Inductive and thematic data analyses were conducted. We interviewed PHC users (n = 17), doctors (n = 16), other PHC staff (n = 7), and non-PHC users (n = 5) and observed the PHC activities. We found that privacy is imperative for both patients and doctors. Design and conditions in PHC, including consultation room doors open, separate rooms for treatment, and patients' symptoms asked by other staff were aspects that undermine privacy in PHC. Inadequate physical and informational privacy protection during a patient's visit has affected the quality of care negatively in ways that impede proper anamneses and physical examination. Ensuring patients' and doctors' physical and informational privacy is essential to creating PHC as the primary source of care that responds to the privacy values of its users, but it has been overlooked. The PHC building designs and care provision guidelines should incorporate the privacy needs of patients and doctors.

The Design of a Safe Charging System Based on PKS Architecture

With the development of new energy vehicles, information sharing and charging service-sharing in the Internet of Vehicles have become popular directions in smart city research. The number of new energy vehicles has surged, and the ensuing range anxiety and low charging efficiency have become the main problems to be solved urgently in charging services. In the era of big data, privacy leakage is becoming more and more serious, and information security and privacy protection cannot be delayed. This paper proposes an efficient charging and privacy protection system based on the PKS system. The original single-stage topology is improved by adding the PFC and LLC circuit topologies. The PID method is used to precisely control the voltage and current loss to improve the conversion efficiency of the charging pile. The private data in the shared information uses the RSA encryption algorithm to prevent the leakage of private data and enhance the security of system communication. This paper aims to improve the charging efficiency of charging piles and the security of private information in network communication. Simulation experiments are carried out on the proposed hardware topology and software encryption system scheme. Experiments compare the waveform state of the improved output current and voltage and the safety protection area of the system architecture. The results show that the proposed charging system is efficient and safe.

데이터 보안의 함의(含意)와 입법방향에 관한 소고

In this article, data security is considered almost identical to cybers ecurity. The development of information and communication technolo gy contributed to the transition to an information society. However, t he protection of information security, privacy, and freedom of inform ation in cyberspace has become an important role for the nation. However, Korea does not have a legal system that governs data se curity in cyberspace yet. Therefore, it is impossible to systematically carry out risk management tasks for cyberspace. Against this backgr ound, the need to legislate the data security law is being raised. In this article, the author analyzed the implications of data securit y. And the data security laws of major countries were analyzed and compared with that of Korea. Through this process, implications we re drawn. The result is as follows. Firstly, an integrated data security law that encompasses the public and private sectors should be enact ed. Secondly, the data security law should include special provisions on the processing of personal information. Thirdly, cooperation with the international community should be strengthened. Fourthly, comm unication should be strengthened in the legislative process to reach a social consensus. In addition, the subject of political disputes in the legislative proc ess should be avoided. And the basic values of the constitution must be protected.

Computer information security and privacy protection technology in the background of big data

At present, information technology has been widely used in many fields. Although it has gradually got rid of the restrictions of traditional management mode, more security problems have emerged, which has created a new way for network crime and caused a negative impact on the steady development of society. In order to fully demonstrate the application value of computer technology under the background of big data era, strengthening privacy and protection and control based on information security risks are the main contents of current scientific research and discussion. In this article, therefore, understand the era of big data and information security problems and influencing factors of the computer, according to the accumulated experience of technological innovation in recent years, the key analysis certificate authority system, credit mechanism, comprehensive protection, these three aspects of the privacy protection technology, and from the perspective of the current computer technology application, this paper puts forward the effective protective measures

Computer information security and privacy protection technology in the background of big data

At present, information technology has been widely used in many fields. Although it has gradually got rid of the restrictions of traditional management mode, more security problems have emerged, which has created a new way for network crime and caused a negative impact on the steady development of society. In order to fully demonstrate the application value of computer technology under the background of big data era, strengthening privacy and protection and control based on information security risks are the main contents of current scientific research and discussion. In this article, therefore, understand the era of big data and information security problems and influencing factors of the computer, according to the accumulated experience of technological innovation in recent years, the key analysis certificate authority system, credit mechanism, comprehensive protection, these three aspects of the privacy protection technology, and from the perspective of the current computer technology application, this paper puts forward the effective protective measures

Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data

AbstractThe protection of private information is of vital importance in data-driven research, business and government. The conflict between privacy and utility has triggered intensive research in the computer science and statistics communities, who have developed a variety of methods for privacy-preserving data release. Among the main concepts that have emerged are anonymity and differential privacy. Today, another solution is gaining traction, synthetic data. However, the road to privacy is paved with NP-hard problems. In this paper, we focus on the NP-hard challenge to develop a synthetic data generation method that is computationally efficient, comes with provable privacy guarantees and rigorously quantifies data utility. We solve a relaxed version of this problem by studying a fundamental, but a first glance completely unrelated, problem in probability concerning the concept of covariance loss. Namely, we find a nearly optimal and constructive answer to the question how much information is lost when we take conditional expectation. Surprisingly, this excursion into theoretical probability produces mathematical techniques that allow us to derive constructive, approximately optimal solutions to difficult applied problems concerning microaggregation, privacy and synthetic data.

Promoting Information Privacy Protection Awareness for Internet of Things (IoT)

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

Protecting Private Information for Two Classes of Aggregated Database Queries

An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise.

E-Government and Privacy (Challenges and Solutions)

E-government, with all its advantages in improving government processes, reducing costs, reducing bureaucracy, creating an e-community and facilitating communication with citizens, also has its weaknesses. The most important concern about the development and growth of e-government is the violation of citizens' rights in cyberspace. One of the most important citizenship rights in cyberspace is the right to information privacy. Information privacy is a right. The right that individuals have to control their information against misplaced searches, eavesdropping, spying, misappropriation and misuse of personal information. In this article, the concept and models of e-government are examined and the concept of citizens' information privacy and its relationship with e-government is stated. For citizens' information privacy in e-government, the challenge of information privacy and e-government maturity, the challenge of information privacy and public interest, the protection of information privacy and freedom of information and the challenge of granting citizens the right to access information in e-government are the most important challenges of this study. have been identified

Influence of corporate social responsibility considering dual-channel recycling competition and privacy information protection

When conducting transactions of recycling, consumers are now paying more attention to information security, concerning the problem of information leakage. From this perspective, we aim to testify whether constructing offline stores combined with privacy information protection to guarantee information security is worthwhile, and what is the effects of Corporate social responsibility (CSR) on profits of competing companies with dual-channel recycling. We build up models to examine the effects of the offline store construction integrated with privacy information protection on profits of companies. Moreover, we construct several models with the total utility of the enterprise to further evaluate the effect of CSR, which composes of the net profit and the CSR utility under different CSR consideration. The CSR is taken by no entities, only entity with offline channel, only entity with online channel and both entities, respectively. The results show the competing companies could gain the most earnings in the situation that both channels consider CSR, while only the offline channel considers CSR is the worst choice. In addition, the revenue-sharing contract and cost-sharing contract could optimize the profit of enterprise constructing privacy information protection, and the former is better than the latter. This study gives insights into the construction of privacy information protection and the consideration of CSR activities of enterprises, which in turn help boost the recycling process and achieve environmentally sustainable of the recycling industry.

Robust privacy‐preserving federated learning framework for IoT devices

Federated Learning (FL) is a framework where multiple parties can train a model jointly without sharing private data. Private information protection is a critical problem in FL. However, the communication overheads of existing solutions are too heavy for IoT devices in resource-constrained environments. Additionally, they cannot ensure robustness when IoT devices become offline. In this paper, Democratic Federated Learning (DemoFL) is proposed, which is a privacy-preserving FL framework that has sufficiently low communication overheads. DemoFL involves a consensus module to ensure the system is robust. It also utilizes a tree structure to reduce the time communication overheads and realizes high robustness without reducing accuracy. The proposed algorithm reduces the communication complexity of aggregation at training by M $M$ times, M $M$ being a controllable parameter. Sufficient experiments have been conducted to evaluate the efficiency of the proposed method. The experimental results also demonstrate the practicality of the proposed framework for IoT devices in unstable environments.