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Mobile Applications (a.k.a. apps) have rapidly grown into a multi-billion-dollar industry. Because they are available through devices that are “always on” and often with the user, mobile apps are often adopted “on the fly” as needed. As a result, the related adoption and disclosure decisions are frequently only based on the information provided through the mobile app delivery platform (e.g., the Apple App Store™ or Google Play™). The fact that mobile app use often requires the disclosure of an unprecedented combination of personal information (e.g., location data, preferences, contacts, calendars, browsing history, music library) means that a very complex risk/benefit tradeoff decision is based on only the small amount of information provided by the mobile app delivery platform — and all in a short period of time. Hence, this process is much shorter and much riskier than traditional software adoption. Through two experiments involving 1,588 mobile app users, we manipulated three primary sources of information provided by the platform (app quality ratings, network size, and privacy assurances) to understand their effect on perceptions of privacy risks and benefits, and in turn, how they influence consumer adoption intentions and willingness-to-pay (WTP). The results indicate that network size influences not only perceived benefits but also the perceived risks of apps in the absence of perfect information. In addition, we find that integrating a third-party privacy assurance system into the app platform has a significant influence on app adoption and information disclosure. LBS privacy risk perceptions can also be reduced by network size, thus confirming our information cascade hypothesis. We further discuss the implications of these findings for research and practice.

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control—to name just a few—are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: privacy of personal information, trust, consent to provide information, and multi-party privacy. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones may exhibit their applications preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.

Abstract. The amount of personal information shared online every day by youths without control has raised concerns about their vulnerability to privacy disclosure. Parental mediation strategies have been considered as important aspects that can contribute to the reduction of online-related risks. However, research on this field has shown puzzling results and there is not a consistent investigation of the specific role of parental mediation in youths’ online privacy management. Therefore, this systematic review was aimed at examining the evidence for associations between different parental mediation strategies and aspects of both privacy disclosure and protection. The search was conducted in December 2019 in the SCOPUS and Web of Science scientific databases and resulted in 17 studies that have been included in our review. Taken together, the results confirm that mediation practices can be considered as important variables for the reduction of information disclosure and for enhancing privacy protection. However, some specificities emerged. Restrictive forms of mediation showed clear negative associations with disclosing behaviors, whereas active strategies showed more contrasting results. Moreover, restrictive practices seemed to be less linked to attitudes and beliefs of privacy protection than active forms. More research is needed concerning the role of co-viewing strategies. Findings were discussed in terms of future research and intervention.

We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED., 11 pages, 15 figures

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey.

Although collecting personal information about consumers is crucial for firms and marketers, understanding of when and why consumers accept or reject information collection remains limited. The authors conceptualize a privacy calculus that represents a consumer’s trade–off of the valence and uncertainty of the consequences of the collection, storage, and use of personal information. For example, usage-based car insurance requires drivers to share data on their driving behavior in exchange for a discount (certain benefit) but at the risk of third parties intercepting location data for malicious use (uncertain disadvantage). Building on this conceptualization, the authors develop the privacy calculus (PRICAL) index. They empirically confirm the validity of the items (Study 1) and the index as a whole (Study 2). The PRICAL index is generally applicable and improves the explanation of behavioral intentions (Study 2) and actual behavior (Study 3), compared with currently used constructs (e.g., privacy concern, trust). Overall, the PRICAL index allows managers to understand consumers’ acceptance of information collection regarding financial, performance, psychological, security, social, and time-related consequences, which the authors demonstrate using the top five most valuable digital brands (Study 4).

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which in turn makes IoT networks an open-source of Personally Identifiable Information data for malicious attackers to steal, manipulate and perform nefarious activities. Huge interest has developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data is widely available and transferable to the main server because data is born at the edge and is generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose federated learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on Gated Recurrent Units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of the FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

In blockchain technology, data are stored on decentralized nodes and public to each node in the blockchain network. Hence, the focus of privacy protection in the blockchain guarantees the anonymity of transactions such that attackers cannot attain the users’ personal information through data analysis. Among the existing privacy protection technologies, the scheme based on group signature has good anonymity, but the existing scheme exists a large number of operations that are difficult to apply to wireless terminals. In this paper, using the powerful offloading capability of edge computing, we propose a blockchain node traceable identity privacy technology scheme based on threshold group signature, and the scheme greatly reduces the computing burden of nodes while achieving node privacy protection.

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual's location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual's PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users. © 2021 Tech Science Press. All rights reserved.

PurposeIn the era of big data, people are more likely to pay attention to privacy protection with facing the risk of personal information leakage while enjoying the convenience brought by big data technology. Furthermore, people’s views on personal information leakage and privacy protection are varied, playing an important role in the legal process of personal information protection. Therefore, this paper aims to propose a semi-qualitative method based framework to reveal the subjective patterns about information leakage and privacy protection and further provide practical implications for interested party.Design/methodology/approachQ method is a semi-qualitative methodology which is designed for identifying typologies of perspectives. In order to have a comprehensive understanding of users’ viewpoints, this study incorporates LDA & TextRank method and other information extraction technologies to capture the statements from large-scale literature, app reviews, typical cases and survey interviews, which could be regarded as the resource of the viewpoints.FindingsBy adopting the Q method that aims for studying subjective thought patterns to identify users’ potential views, the authors have identified three categories of stakeholders’ subjectivities: macro-policy sensitive, trade-offs and personal information sensitive, each of which perceives different risk and affordance of information leakage and importance and urgency of privacy protection. All of the subjectivities of the respondents reflect the awareness of the issue of information leakage, that is, the interested parties like social network sites are unable to protect their full personal information, while reflecting varied resistance and susceptibility of disclosing personal information for big data technology applications.Originality/valueThe findings of this study provide an overview of the subjective patterns on the information leakage issue. Being the first to incorporate the Q method to study the views of personal information leakage and privacy protection, the research not only broadens the application field of the Q method but also enriches the research methods for personal information protection. Besides, the proposed LDA & TextRank method in this paper alleviates the limitation of statements resource in the Q method.

This paper examined the formation of litigation intentions among e-commerce customers under the privacy breach due to the influence of antecedents such as vulnerability, social risk, privacy dispositions, effectiveness privacy policy, awareness of data management and moderators like privacy control beliefs, efficacy in coping and litigation complexity. A structural equation modelling analysis revealed that reasons for privacy breach perceptions are customer dispositions about privacy, anticipated vulnerability due to privacy breach, and social risk related to personal information disclosure. The control beliefs and coping skills of customers under privacy threat positively moderates litigation intentions. Similarly, the task complexity of litigation significantly reduces litigation intentions.

Background: Data breaches happen when an unauthorized party gains access to personally identifiable information. They are becoming more common and impactful, raising serious concerns for individuals as well as companies. Literature review: Although there is considerable literature on users’ mental models in security and privacy, there has been limited study of mental models related to data breaches. Research questions: 1. How do users understand data breaches? 2. What are their perceptions of the causes, responsibilities, and consequences, as well as possible prevention and appropriate follow up? Methodology: We explored end-user understanding of internet data breaches by conducting a study with 35 participants. They were asked to draw their understanding of data breaches and answer some open-ended and closed-ended questions afterwards. Results/discussion: Although their drawings varied in detail and complexity, we identified four patterns in the participants’ drawings: they illustrated abstractions of attacks to gain administrator access, end-user access, backdoor access, or access using database server vulnerabilities. We found that participants had a basic model of how an internet data breach happens, but with significant uncertainties regarding system vulnerabilities, causes, consequences, prevention methods, and follow-up steps after a breach. Conclusions: In all, end-user mental models of internet data breaches are basic and show gaps that emphasize the need for improved communication to increase users’ awareness and help them hold companies accountable.

In Code, the most influential book yet written about law and cyberspace, Lawrence Lessig makes an intriguing proposal for shaping privacy on the Internet: (1) the legal assignment to every individual of a property interest in her own personal information, and (2) the employment of software transmission protocols, such as P3P, to permit the individual to structure her access to Web sites. In "Beyond Lessig's Code for Internet Privacy: Cyberspace Filters, Privacy Control, and Fair Information Practices," 2000 Wisc. L. Rev. 743, I respond to this approach with a number of criticisms and a competing proposal. My initial criticism of Lessig's proposal for privacy concerns how it contradicts his stand against PICs, a software transmission protocol for filtering Internet content reminiscent of P3P. Once we place privacy in a social context, moreover, P3P seems far less attractive an option. In place of Lessig's underlying paradigm, which seeks to increase personal control of data. I develop a concept of constitutive privacy. In my view, information privacy is a constitutive value that safeguards participation and association in a free society. Rather than simply seeking to allow more and more individual control of personal data, we should view the normative function of information privacy as inhering in its relation to participatory democracy and individual self-determination. A privacy market can play a role in helping information privacy fulfill this constitutive function. Yet, Lessig's propertization of privacy raises a further set of difficulties. In my view, propertization a la Lessig will only heighten flaws in the current market for personal data. This consequence follows from numerous shortcomings in this market and structural difficulties that indicate the unlikelihood of a self-correction in it. Moreover, in revisiting Calabresi and Melamed's work regarding the comparative merits of property and liability regimes, I find that a mixed regime is to be preferred for Internet privacy over Lessig's property regime. Part III of this Article turns from criticism to prescription and develops the mixture of property and liability rules necessary for establishment of information privacy standards in cyberspace. It proposes recourse to Fair Information Practices (FIPs) to establish rules for the fair treatment of personal data on the Internet. Yet, FIPs are not without potential shortcomings if structured only as command-and-control rules. My suggestion therefore is that an American Internet privacy law consisting of FIPs should include both mandatory and default elements.

Proposed "information privacy" rules that give us the power to "control . . . information about ourselves" and to mandate "fair information practices" sound appealing. Why, after all, should a business be able to communicate information about its clients without the clients' permission, and why should the media be able to publish unnewsworthy private information about us for the whole world to see? The difficulty is that the right to information privacy -- the right to control other people's communication of personally identifiable information about you -- is a right to have the government stop people from speaking about you. And the First Amendment (which is already our basic code of "fair information practices") generally bars the government from "control[ling the communication] of information," either by direct regulation or through the authorization of private lawsuits. This article makes two main points: (1) While privacy protection secured by contract turns out to be constitutionally sound, broader information privacy rules are not easily defensible under existing free speech law. (2) Creating new free speech exceptions to accommodate information privacy speech restrictions could give us much more than we bargained for: Most of the justifications given for information privacy speech restraints are directly applicable to other speech control proposals that have already been proposed, and accepting these justifications in the attractive case of information privacy speech restrictions would create a powerful precedent for those other restraints. My chief goal is to consider, as concretely as possible, what these unintended consequences of various justifications for information privacy speech restrictions might be. I ultimately conclude that these consequences are troubling enough that I must reluctantly oppose such information privacy rules. But I hope the article will also be useful to those who are committed to supporting information privacy speech restrictions but would like to design their arguments in order to minimize the risks that I identify, and even to those who welcome the possibility that information privacy speech restrictions may become a precedent for other restrictions because they believe the Court has generally gone too far in protecting, say, nonpolitical speech or speech that injures the dignity of others.

Ubiquitous mobile sensors on human activity recognition pose the threat of leaking personal information that is implicitly contained within the time-series sensor signals and can be extracted by attackers. Existing protective methods only support specific sensitive attributes and require massive relevant sensitive ground truth for training, which is unfavourable to users. To fill this gap, we propose a novel data transformation framework for prohibiting the leakage of sensitive information from sensor data. The proposed framework transforms raw sensor data into a new format, where the sensitive information is hidden and the desired information (e.g., human activities) is retained. Training can be conducted without using any personal information as ground truth. Meanwhile, multiple attributes of sensitive information (e.g., age, gender) can be collectively hidden through a one-time transformation. The experimental results on two multimodal sensor-based human activity datasets manifest the feasibility of the presented framework in hiding users' sensitive information (inference MAE increases ∼ 2 times and inference accuracy degrades ∼ 50%) without degrading the usability of the data for activity recognition (only ∼2% accuracy degradation).

COVID-19 has presented unusual challenges for individuals, governments and societies across the globe. Several non-medical and non-pharmaceutical interventions have demonstrated to be critical in addressing the resultant impacts. One notable tool among these interventions is the application of technology in identifying infected persons or individuals coming into contact with those infected. Policy think-tanks have invested in geospatial technology and information systems to help resolve contact tracing inefficiencies to curtail the fast spread of the disease. This study highlights the extent of the application of geospatial technology in COVID-19 contact tracing in Ghana. Here, it was demonstrated that majority of young adults that form the greater part of Ghana’s population have access to digital devices which serve as primary catalysts in facilitating effective and efficient contact tracing. Case count of the pandemic continues to surge sharply from one month to the other since the first recorded case on March 12, 2020. A huge number of cases were recorded in the southern part of the country, as against cases recorded in the north. Mobility patterns depicted the migration of more people from regions with a high number of case count to regions with lower counts. We recommend a holistic and proactive approach to the use of smart mobile devices and applications in enhancing contact tracing. Privacy and data protection laws must be prioritized and supported by effective legislative and policy frameworks that serve as the legal basis for the management of personal information.

Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times. Innocent Internet user if peradventure clicking on a fraudulent link may cause him to fall victim of divulging his personal information such as credit card pin, login credentials, banking information and other sensitive information. There are many ways in which the attackers can trick victims to reveal their personal information. In this article, we select important phishing URLs features that can be used by attacker to trick Internet users into taking the attacker’s desired action. We use two machine learning techniques to accurately classify our data sets. We compare the performance of other related techniques with our scheme. The results of the experiments show that the approach is highly effective in detecting phishing URLs and attained an accuracy of 97.8% with 1.06% false positive rate, 0.5% false negative rate, and an error rate of 0.3%. The proposed scheme performs better compared to other selected related work. This shows that our approach can be used for real-time application in detecting phishing URLs.

Through voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. g., geographical origin, health status, personality). We conducted a nationally representative survey in the UK (n = 683, 18–69 years) to investigate people’s awareness about the inferential power of voice and speech analysis. Our results show that – while awareness levels vary between different categories of inferred information – there is generally low awareness across all participant demographics, even among participants with professional experience in computer science, data mining, and IT security. For instance, only 18.7% of participants are at least somewhat aware that physical and mental health information can be inferred from voice recordings. Many participants have rarely (28.4%) or never (42.5%) even thought about the possibility of personal information being inferred from speech data. After a short educational video on the topic, participants express only moderate privacy concern. However, based on an analysis of open text responses, unconcerned reactions seem to be largely explained by knowledge gaps about possible data misuses. Watching the educational video lowered participants’ intention to use voice-enabled devices. In discussing the regulatory implications of our findings, we challenge the notion of “informed consent” to data processing. We also argue that inferences about individuals need to be legally recognized as personal data and protected accordingly.

Systems drawing on databases of personal information increasingly shape life experiences and outcomes across a range of settings, from consumer credit and policing to immigration, health, and employment. How do these systems identify and reidentify individuals as the same unique persons and differentiate them from others? This article advances a general sociological theory of personal identification that extends and improves earlier work by theorists like Goffman, Mauss, Foucault, and Deleuze. Drawing on examples from an original ethnographic study of identity theft and a wide range of social scientific literature, our theory treats personal identification as a historically evolving organizational practice. In doing so, it offers a shared language, a set of concepts for sensitizing researchers’ attention to important aspects of personal identification that often get overlooked while also facilitating comparisons across historical periods, cultural contexts, substantive domains, and technological mediums.

Parental-control solutions often require dangerous privileges to function. We analyzed privacy/security risks of popular solutions and found that many leak personal information and are vulnerable to attacks, betraying the trust of parents and children.

Medical applications are fast growing in popularity, both for professional use and for patient-centered apps. The current Health record and IoT data sharing systems however have a fair amount of problems associated with privacy and security. The secure and privacy aware E-Health record to propose a mechanism using blockchain and IPFS (InterPlanetary File System) which offers a solution to all these problems. It also includes limitations and safeguards on what can be done and cannot be done with your personal information in some cases. The IPFS data will be dispersed among the nodes. Use of IPFS (Interplanetary File System) to store health records, which has the benefit of being distributed which in turn makes record tamper-free. In addition, the proposed model keeps track of disease statistics without invading any patient's privacy. This is conceivable not only for web pages, but for any type of file that a computer might save, whether it's a document, an email, or even a database record, thanks to IPFS.

Adolescents of today are at risk of online disclosure due to sharing personal information online without privacy settings. Facebook has the most users on social network site. This study aimed to 1)...

Defined as someone using other’s personally identifiable information to make profit or commit crimes, identity theft has become a critical problem for the whole society. The development of Internet technology has made this problem worse. Results of empirical assessment including non-linear quadratic effect prove that when users have the perception of more control over the identity theft threat, they are likely to find solutions, feel it is their responsibility and has more intentions for identity theft prevention actions to prevent identity theft. Interestingly, our theory and empirical results suggest that quadratic effect exist among critical constructs in our theoretical model and that the underlying complexity require further investigation and that linear models may not be necessarily sufficient. We further contribute by developing theory and empirical validation of non-linear quadratic effect among key constructs related to Identity theft in the context of IT security literature.

Purpose Wine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness. Design/methodology/approach The study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020. Findings The authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction. Research limitations/implications The limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires. Practical implications The proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas. Social implications The proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas. Originality/value The methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.

Digital network platforms are built on sociotechnical interaction between actors and actors. The creation and development of new public services based on digital platforms inevitably leads to the transformation of the relationship between the state and citizens. The attractiveness of state digital platforms for citizens increases when resolving the contradiction between the possibilities of new forms of social interaction and the threat of misuse of personal data, the risk of harm or persecution.The article presents the results of the analysis of the boundaries of the public and private in the interaction of the state with citizens on digital network platforms. The research method is a comparative analysis, which is based on the dichotomy of public and private, reflected in the concept of private and public X. Arendt, concepts of the public sphere J. Habermas, regulatory and legal concepts of privacy by R. Gavison. The empirical base was made up of a sociological study conducted to obtain information about the boundaries of privacy and publicity of personal data in the digital network space (n = 1 000 among the population over 18 years old living in metropolitan megacities and median regions by the level of informatization, 2020) and the results of Kaspersky Lab surveys conducted in 2019–2020.The conducted research allows us to assert that almost 2/3 of citizens have faced the misuse of confidential information on the Internet. Most of the respondents are aware that websites, social networks and search engines can collect data for web analytics. At the same time, citizens consider it possible to transfer personal data to the authorities in a generalized form for making managerial decisions. Half of the surveyed population does not object to the implementation of digital control over the actions and movements of citizens. Thus, despite the existing negative experience, it is unlikely that there will be any obvious resistance to organizing the collection of personal information on digital network platforms.

Az OSINT (Open Source Intelligence) mozaikszó mint a nyílt forrású hírszerzés jelentősége az elmúlt években egyre nagyobb szerepet kap az egyes szolgálatok életében, hiszen a digitális információk megszerzése és birtoklása új kihívások elé állítja a szakembereket. A legtöbben biztosan hallottuk már azt a közhelyet, miszerint „ami egyszer felkerül az internetre, az ott is marad”. Ezt szem előtt tartva kell mindennapi munkánkat végeznünk, hiszen soha nem tudhatjuk, hogy ki és mikor használhat fel ellenünk olyan személyes információkat, amelyek megszerzéséhez mi magunk szolgáltattuk az adatokat valamikor a közel- vagy régmúltban. Jelen írásban az OSINT jelenetőségének bemutatását követően a digitális tudatosság fontosságát hangsúlyozva rámutatok néhány tipikus gyenge pontra, úgymint a gyermekek és az idősebb korosztály e téren hiányos ismeretei, az operációs rendszerek, más programok adatgyűjtései, a kiszivárgott vagy gyűjtött adatbázisok jelentette veszélyek, az adatvesztés körülményei és következményei. Kitérek továbbá az egyre bővülő speciális adatgyűjtő célszoftverekre is. Ezek kapcsán megoldási javaslatokat vázolok fel, amelyek hasznos segítséget jelenthetnek akár a prevenciót, akár a veszteségek minimalizálását illetően.

Mobile usage has become a part of our daily lives. However, many mobile users ignore the existence of app permissions in an app. One of the reasons is the lack of information related to app permissions. If users are not careful, app permissions can be abused by hackers to steal their data such as SMS, photos, microphone access, and GPS. The purpose of this study is to observe the perspective of university students in Batam regarding app permissions as well as to see the consequences of that perspective. The basis of this research is using survey method for 132 social media users and regression. This study proves that mobile users’ information privacy concerns have an influence on the intention to accept app permissions, which means that university students of Batam tend to consider personal information privacy on mobile before accepting app permissions. However, computer anxiety and perceived control do not affect mobile users’ information privacy concerns, which means university students of Batam have neither computer anxiety nor perceived control over personal information privacy on mobile.

On 10 June 2021, the Data Security Law of the People’s Republic of China (hereinafter the “DSL”) was adopted at the 29th session of the Standing Committee of the 13th National People’s Congress, effective as of 1 September 2021. The DSL is the fundamental law in the data security sphere and, together with the Cybersecurity Law (hereinafter the “CSL”) and the Personal Information Protection Law (hereinafter the “PIPL”), outlines the data regulatory framework in China. The DSL contains seven chapters and 55 articles that widely cover data security mechanisms, obligations and liabilities at both State administration and data handler levels. In this article, the key contents of the DSL together with the intensively promulgated supplemental laws and regulations will be analyzed to provide a comprehensive grasp of data security supervision in China. Specifically, section one explains the basic concepts of the DSL, section two highlights the key data security protection mechanisms such as the Important Data protection and data cross-border transfer and section three will summarize the main compliance obligations for companies to effectively put laws into actions.

Purpose Many organizations are challenged by different and, perhaps, opposite, registration and protection obligations of information regarding their employees. The purpose of this paper is to explore how organizations balance the registration obligations of the Icelandic equal pay standard (EPS) and the protection requirements of the general data protection regulation (GDPR). It aims to raise awareness of how information professionals can ensure that documentation on the education and skills of employees is authentic, traceable and secure. Design/methodology/approach The analytical framework covered multiple-cases and semi-structured interviews with various professionals and comprehensive documentary analysis. Findings The findings indicate that the organizations were not properly prepared for the implementation of the EPS and were hesitant regarding further registration of personal information due to GDPR. Documentary analysis also revealed critical attitudes towards the legal endorsement of the standard and its potential success. Originality/value There is a lack of studies explaining the juxtaposition of information and records management and the legal and regulatory environment. This paper provides a unique description of how information and recordkeeping practices function with the requirements of the EPS whilst complying with GDPR. The results could bring valuable opportunities for the information profession regarding the development, implementation, administration and maintenance of documentary evidence regarding the requirements of international and national standards and legislations and advance their collaboration with other professionals in the management of information.

In this article, the study of the Deep Web – "Deep Web" is carried out. The article examines the way how this network appeared and spread, considers its positive and negative sides, studies such issues as: the concept, types, forms and characteristics, as well as compares it with the hidden Internet and the "black Internet" (DarkNet) and suggests methods of personal information security.

PurposeThis study aims to understand the benefits and challenges associated with the adoption of a blockchain-based identity management system in public services by conducting an academic literature review, and to explore the design of such a system that can be applied to the Korean government.Design/methodology/approachThis study explores the adoption of a blockchain-based identity management system using a literature review and an actual design case intended for use by the government sector.FindingsBlockchain-based identity management systems can significantly improve transparency, accountability, and reliability in the user control of one's own data while reducing the time and cost needed to deliver public services, as well as increasing administrative efficiency. However, it is not always easy to implement such systems, and introducing new technologies in the government field requires a complicated, time-consuming process. There is currently an appetite for research extending beyond the typical technology-driven approach to elucidate the government adoption of new technologies and explore its implications.Practical implicationsThe idea behind this system is that by storing and managing personal information on the blockchain and providing mobile apps to customers, users can log in or retrieve previously authenticated personal information without having to go through an authentication process. Since users do not need to go through the verification process every time, it is expected that they will be able to access only the necessary personal information more quickly and conveniently without having to deal with unnecessary details. In addition, the blockchain-based operation of a public service effectively increases the transparency and reliability of that service and reduces the social costs caused by personal information leakage.Originality/valueThis study introduces the design of a blockchain-based identity management system that can be used in public services, specifically in the Korean government sector for the first time. Along with a literature review, the implications that this study gleans from these real-world use cases can contribute to this field of research.

The paper considers the main threats to human rights in connection with the introduction of digital profiles in the Russian Federation. Rights such as the right to privacy and the right to dignity are most at risk. In addition, the risk of discrimination increases. Analyzing the current legal regulation of the digital profile, the author concludes that it does not meet the criterion of legal certainty and creates increased risks of intrusion of the state and private structures into the sphere of a person’s private life. Despite the fact that currently digital profiles of citizens are only a set of official information contained in some state information systems and public registers, according to the author, in the future, this infrastructure can be used for profiling people, in-depth analysis, monitoring and forecasting their behavior, as is already done today by some other states and nongovernmental organizations.The legal regulation of the digital profile should be based on special guarantees of human rights in connection with the collection and processing of personal information about citizens available to the state. Among such guarantees, the author includes, in particular, the establishment in the law of a list of information that cannot be part of a digital profile of a citizen or be otherwise related to it, a list of unacceptable purposes for using digital profiles, as well as the establishment of the obligation of operators to inform subjects in an accessible form about the facts and legal consequences of profiling, about the principles and logical schemes underlying profiling.

The Android OS market is experiencing a growing share globally. It is becoming the mobile platform of choice for an increasing number of users. People rely on Android mobile devices for surfing the web, purchasing products, or to be part of a social network. The large amount of personal information that is exchanged makes privacy an important concern. As a result, the trustability of mobile apps is a fundamental aspect to be considered, particularly with regard to meeting the expectations of end users. The rigidities of the Android permission model confine end users into a secondary role, offering the only option of choosing between either privacy or functionalities. In this paper, we aim at improving the trustability of Android apps by proposing a user-centric approach to the flexible management of Android permissions. The proposed approach empowers end users to selectively grant permission by specifying (i) the desired level of permissions granularity and (ii) the specific features of the app in which the chosen permission levels are granted. Four experiments have been designed, conducted, and reported for evaluating it. The experiments consider performance, usability, and acceptance from both the end user's and developer's perspective. Results confirm confidence on the approach.

Many studies are still ongoing on the possible effects of the Covid-19 Pandemic process on educational environments. With the decision to switch to distance education during the pandemic process, an important opportunity has emerged for rapid digital transformation in higher education. In this process, the relationships between the digital literacy levels of university students, their views on distance education and their pre-university school memories are curious. Therefore, the main purpose of this study is to examine the relationship between education faculty students' digital literacy levels and their views on distance education and their pre-university school memories. The research was carried out in the faculty of education at a state university in the 2020-21 academic year. The sample group consists of n = 234 (89 males; 145 females, mean age 21.7) pre-service teachers. Research data were collected online via Classroom. During the data collection process, one personal information form and three scale forms were used together. The return rate of the data collection tool is ~ 51%. Descriptive and relational analysis were used to analyze the collected data (p