Privacy Information Disclosure Research Articles

Drivers and consequences of borrowers' voluntary privacy information disclosure—Evidence from a Chinese online consumer financing platform

AbstractThis study explores the drivers and consequences of consumers' voluntary information disclosure in the context of online consumer financing. Using unique secondary data from a Chinese online consumer financing company, we uncover a non‐linear relationship between borrowers' credit ratings and the quantity of voluntary information disclosure, and an inverted U‐shaped relationship between the amount of information disclosure and loan default. Specifically, low‐credit borrowers who opt for more voluntary information disclosure manifest a higher likelihood of default than those who disclose less, suggesting a strategic inclination toward information disclosure. Finally, we identify that disclosing high‐cost information reduces loan default, whereas disclosing low‐cost information increases default. Our findings enrich the existing literature on consumer information disclosure by providing insights into the underlying motivation and highlighting the applicability of signaling theory and privacy calculus theory. This has practical implications for lenders, borrowers, and regulators in overcoming barriers in online lending.

Smart mobile tourism app featuring augmented reality and big data analytics: an empirical analysis using UTAUT2 and PCT models

Purpose Considering the limited understanding of determinants influencing the adoption of smart mobile tourism app (SMTA) featuring augmented reality (AR) and big data analytics (BDA), privacy concern (PC) and the risk of privacy information disclosure (PI) have threatened SMTA adoption. This study aims to propose an expanded consumer acceptance and use of information technology (UTAUT2) model by including new contextual components, integrated with privacy calculus theory (PCT) model to examine the determinants influencing behavioural intention (BI) to use SMTA. Design/methodology/approach Personal innovativeness (IN) and privacy information disclosure (PI) are incorporated in UTAUT2 model to determine its effect on SMTA featuring AR and BDA technologies from smart perspective. Both privacy concern (PC) and privacy risk (PR) derived from PCT model are also included to determine its influences on an individual's willingness to disclose privacy information for better-personalised services. We collected responses from 392 targeted participants, resulting in a strong response rate of 84.66%. These responses were analysed statistically using structural equation modeling in both SPSS 22.0 and SmartPLS 3.0. Findings Findings showed that personal innovativeness (IN), habit (HT) and performance expectancy (PE) significantly affect behavioural intention (BI) while privacy concern (PC) significantly affect privacy information disclosure (PI) to use SMTA. In contrast, effort expectancy (EE), hedonic motivation (HM) and privacy information disclosure (PI) had no significant effects on behavioural intention (BI) while privacy risk (PR) had no significant effects on privacy information disclosure (PI) to use SMTA. Originality/value The study findings help tourism practitioners in better comprehending recent trends of SMTA adoption for establishing targeted marketing strategies on apps to improve service quality. In addition, it enables app development companies acquire app users’ preferences to enhance their app development for leading app usage.

Research on Digital Information Privacy Behavior of Social Network Users Based on Evolutionary Game

This article takes social network services as the research object and mainly conducts two aspects of research: first, based on previous research, combining the behavior characteristics of users in the social network environment related to digital information privacy, the digital information privacy of users pays attention to the influential factors of users’ digital information privacy information disclosure behavior, summarize and refine them, establish theoretical research models, and then use structural equation modelling to empirically analyze the significance of each influence path; then, based on the first part of the empirical research, the evolutionary game theory was used to analyze the interests of digital information privacy between social network users and service providers. This paper also studies the evolution of the willingness of each parameter to provide digital information to users under variable expected return conditions and uses MATLAB to analyze its evolutionary trends. It is found that the regulatory intensity coefficient, information leakage loss, and information sensitivity are both for users and websites. It has an important impact, and the loss of information leakage and information sensitivity can affect the evolution direction of users’ willingness to provide digital information and change the speed of website evolution, and the regulatory intensity coefficient is the opposite.

Revealing or concealing: privacy information disclosure in intelligent voice assistant usage- a configurational approach

PurposeBased on communication privacy management (CPM) theory, this study aimed to explore how gender and age criteria, motivational criterion (locatability), benefit-risk ratio criteria (perceived benefits of privacy information disclosure and privacy concerns), contextual criteria (privacy experience) and boundary coordination and turbulence (perceived effectiveness of privacy policy) interact and combine in leading to voice assistant (VA) users' intention to reveal or conceal their privacy information.Design/methodology/approachA questionnaire was designed and distributed to car drivers who have intelligent VA use experience while driving. A total of 230 valid responses were collected. The fuzzy-set qualitative comparative analysis (fsQCA) approach was used to analyse the data.FindingsVia configuration analysis with fsQCA, five equivalent configurations leading to the high intention of disclosing privacy information, and one configuration leading to the intention of concealing privacy information were identified. Four theoretical configurational propositions were proposed. Moreover, the theoretical and practical implications of the findings are discussed.Originality/valueThis study offers some insightful implications in the field of private information communication in artificial intelligence applications as well as practical implications for intelligent VA application design. The findings provide empirical evidence of privacy communication management in the context of smart VA usage.

RETRACTED ARTICLE: Location prediction with personalized federated learning

Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data are stored in different clients and different clients cooperate to train to extract useful users’ behavior information and prevent the disclosure of privacy information. Firstly, we put forward an innovative spatiotemporal location prediction framework (STLPF) for location prediction by integrating spatiotemporal information in local and global views on each client and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches.

Personal Information Two-dimensional Code Encryption Technology in the Process of E-commerce Logistics Transportation

The popularity of the Internet has given birth to e-commerce and promoted the development of logistics industry. Traditional logistics is frail in personal information confidentiality, and it is easy to leak privacy information in the process of logistics. This paper briefly introduced the channels of privacy information disclosure in the process of e-commerce logistics and the privacy information encryption system based on two-dimensional code. Then, the privacy protective effect of the system was tested on the laboratory server. The results showed that mobile terminals with different permissions only obtained some necessary logistics information within their respective permissions in the normal process; the mobile terminals distinguished two-dimensional codes that did not belong to express mails, and only meaningless error codes were obtained after the mobile terminal without permissions scanned the code in the abnormal process. In conclusion, the encryption technology of personal information based on two-dimensional code can effectively protect the privacy information in the process of logistics.

Privacy Disclosure in the Real World

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.

Health Privacy Information Self-Disclosure in Online Health Community.

The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care, which has alleviated the above problem. The survival and sustainable development of the online health community requires users to continuously disclose their health and privacy. Therefore, it is a great practical significance to find out the factors and mechanisms that promote users' self-disclosure in the online health community. From the perspective of individual and situation interaction, this study constructed influencing factors model of health privacy information self-disclosure. Finally, we collected 264 valid samples from the online health community through online and offline questionnaire surveys and then use the SPSS20.0 and AMOS21.0 to conduct exploratory factor analysis, confirmatory factor analysis, scale reliability and validity analysis, and structural equation model analysis. The main findings are as follows: trust in websites and trust in doctors reduce the privacy concern. The privacy trade-off will not occur when trust is enough to offset the privacy concerns caused by personalized services, reciprocity norms, and other factors. Second, reciprocity norms are inevitably compulsive, which will increase privacy concerns. However, based on voluntariness, reciprocity norms can enhance user trust. Third, service quality caused by personalized services not only enhance the social rewards of users but also eliminate the privacy concern. Fourth, users' health privacy attention and information sensitivity are too high to decrease the influence of user' privacy concerns on personal health privacy information disclosure. The conclusions of this paper will help us to supplement privacy calculus theory and the application scope of the attention-based view. The proposed strategy of this article can be used to stimulate the information contribution behavior of users and improve the medical service capabilities in online health community.

Application of QR Code Technology in the Design of User Information Privacy Protection Logistics System

This paper takes the protection of user privacy information in express logistics industry as the main research object, and designs a scheme to protect user privacy by using RFID technology and multi-layer encryption technology. The scheme can achieve the dual privacy protection for the internal and external of the logistics company, and ensure that the person responsible for the disclosure of privacy information can be reviewed.

Research on Medical Big Data Application Model Based on Data Separation

Data attributed data, privacy attributed privacy. At present, big data development now going all walks of life, in order to better carry out medical research and so on, the medical industry select development of healthcare big data, but the data are still some big problems in terms of security: user privacy information disclosure and other. This paper proposes a new way of thinking: data separation storage method, by the user's information data into information data block medical data and personal information data blocks, try a way to solve the existing problem of private data leakage, and study the practical application ability of medical big data based on this data separation storage method.

A blockchain approach to digital archiving: digital signature certification chain preservation

PurposeThe short lifespan of digital signatures presents a challenge to the long-term preservation of digitally signed records. It can undermine attempts to presume, verify or assess their authenticity. This paper aims to investigate the challenges of the expiration of digital signatures in the context of digital archiving.Design/methodology/approachThe paper identifies requirements for the long-term preservation of digitally signed records and compares them with the existing approaches. The characteristics, operational procedures and requirements of the technologies used for digital signatures are combined with the archival requirements to design a new model.FindingsThe paper proposes a new model of a blockchain-based system, which can be combined with any digital archive to assist the process of long-term preservation of digitally signed records.Practical implicationsThe proposed model offers a new alternative to the current practice in the long-term preservation of digitally signed records, such as periodic resigning procedures or periodic wrapping of digitally signed records with archival timestamps.Originality/valueThe proposed TrustChain 2.0 model is based on previous research conducted as part of the InterPARES Trust project. It builds on TrustChain 1.0 by including digital signature certificate chain validity information in a blockchain thus avoiding the issues concerning records confidentiality and privacy information disclosure. The paper contributes not only to the development of archival science but also shows archival institutions on how to approach long-term preservation of digitally signed records.

Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices’ electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy $ {k}$ -means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum’s algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on $ {k}$ -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy $ {k}$ -means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy

Social network contains the interaction between social members, which constitutes the structure and attribute of social network. The interactive relationship of social network contains a lot of personal privacy information. The direct release of social network data will cause the disclosure of privacy information. Aiming at the dynamic characteristics of social network data release, a new dynamic social network data publishing method based on differential privacy was proposed. This method was consistent with differential privacy. It is named DDPA (Dynamic Differential Privacy Algorithm). DDPA algorithm is an improvement of privacy protection algorithm in static social network data publishing. DDPA adds noise which follows Laplace to network edge weights. DDPA identifies the edge weight information that changes as the number of iterations increases, adding the privacy protection budget. Through experiments on real data sets, the results show that the DDPA algorithm satisfies the user’s privacy requirement in social network. DDPA reduces the execution time brought by iterations and reduces the information loss rate of graph structure.

위치기반서비스 이용에 대한 인지된 위험의 영향 연구

개인정보 관련 사건사고가 증가하면서 개인정보 기반의 모바일 서비스에 대한 우려가 늘고 있다. 본 연구는 스마트폰 보급 확대로 이용이 증가하고 있는 위치기반서비스 앱 이용에 영향을 미치는 요인을 도출하고 이에 대한 인지된 위험의 영향력을 확인하고자 하였다. 이를 위해 위치기반서비스 앱 이용 행위에 영향을 미치는 변수를 도출한 후 인지된 위험이 이들 변수와 앱 이용 행위 사이에 어떤 영향을 미치는가를 실증 분석하였다. 분석 결과, 유용성과 사회적 영향은 위치기반서비스 앱 이용에 영향을 미치나, 인지된 위험을 높게 인식하는 집단은 유용성으로부터, 인지된 위험을 낮게 인식하는 집단은 사회적 영향으로부터 이용 행위에 더 크게 영향을 받는 것으로 나타났다. 위치기반서비스 산업의 성장을 위해서는 인지된 위험의 교섭력을 최소화하기 위한 정보보호 정책과 보호기술이 요구되며 기업 입장에서는 인지된 위험 수준에 따른 개발 전략을 취해야 할 것이다. As the disclosure of privacy information has grown steadily, concerns about mobile services based on the personal information also increased. We aspired to reveal factors influencing the use of Location-Based services(LBS) App and analyse how the perceived risk affected between these factors and the use of LBS App. Results showed that usefulness and social influence influenced on the use of LBS App. We also found that the group who highly recognized the perceived risk was highly affected by usefulness and the group who lowly recognized the perceived risk was highly affected by social influence. Findings show that the company's strategy should be different depending on the level of consumers' perceived risk.

Privacy Protection in E-commerce: Identity-based Anonymous Privacy Agent

An identity-based anonymous agent privacy protection scheme is proposed to deal with the problems of privacy information disclosure. In this paper, some analysis about the status quo of the privacy protection research in China and some partitioning of privacy data in ecommerce are given. We further illuminate the whole process of privacy protection by an ecommerce privacy protection scheme based on the identity of the anonymous agent. At last, the simulation results are analyzed. Experimental results show that the novel scheme can ensure the anonymous reliability, integrity and efficiency to realize privacy protection in ecommerce process.

Understanding the Factors that influence Website Retention and Privacy Unconcern After the Disclosure of Privacy Information

The purpose of this study is to find an answer why internet users are unconcern about their privacy information. We found that perceived privacy risk and website usability have a significant effect on privacy unconcern. That is, individuals who have experiences privacy incidents are more likely to be unconcern about their privacy information. Accordingly, organizations who supply services on the web have to pay more attention to these individuals to increase a privacy concern. Implications and Conclusions are discussed.

LooM

PurposeThe paper's aim is to present a novel anonymity quantification method, LooM (loosely managed privacy protection method) for achieving privacy protection in pervasive computing environments.Design/methodology/approachThe main feature is that the method quantitatively controls anonymity by a single value (disclosure threshold value) using a classification algorithm of the decision tree. The value is not affected by the set size of users or the distribution of users' private information. The effectiveness of this method is confirmed by simulation using sample databases of attribute‐value pairs. Proposes a model of privacy information disclosure that achieves a balance between users' privacy protection requirements and service providers' disclosure requirements and applies web questionnaire survey data to this model.FindingsLooM can be applicable to a variety of pervasive computing and communication services handling a huge amount of data containing privacy information.Originality/valueThe paper proposes a model of privacy information disclosure that achieves a balance between users' privacy protection requirements and service providers' disclosure requirements.