Search

In the smart city planning based on spatiotemporal big data, the mobile phone signaling big data is the most commonly used data source at the moment. This kind of big data has time and space dimensions and also significant human behavior attributes. According to the relevant Chinese law, the data has been anonymized before sharing, i.e. cannot be identified as a specific individual and cannot be restored again, thus is no longer regarded as personal information. In smart city planning, the mobile phone signaling big data is used to construct the basic dynamic analysis framework of "space-time-behavior". Even if the mobile phone signaling big data has been processed anonymously, it will inevitably show some specific location attribute information of mobile phone users. The anonymous track information can be matched to the corresponding geographical space, so as to mark the active location information of the information subject in a specific period of time. It can easily identify the specific location information such as the job and residence of mobile phone user, and even give user portrait. Existing technology shows that the mobile phone signaling big data is easy to be de-anonymized, and Anonymity rule are not applicable to the sharing of mobile phone signaling big data in the smart city planning. Mobile phone signaling big data belongs to personal sensitive information. Once leaked or abused, it is easy to infringe personal privacy of information subject. Therefore, only using current anonymization means to share the mobile phone signaling big data are not enough to protect the security of personal information in smart city planning, and sharing the mobile phone signaling big data should follow the basic principle of explicit informed consent. In special circumstances or scenarios, breaking through the basic principle of the mobile phone signaling big data sharing should have clear legal provisions and comply with legal procedures.

The rapid evolution of Internet use has led to the collection of big data about users, which has raised users’ privacy concerns about their personal information. This study adopts the hypothesis to evaluate the perception of the information sensitivity of Internet users in Saudi Arabia as research subjects. This study analyzes the sensitivity of 35 types of information through a questionnaire with answers from 508 participants from Saudi Arabia and estimates the cultural influence by comparing Saudi results with users from the USA, Brazil and Germany. Demographic information, such as age, gender, marital status and education, and attitude characteristics, such as disposition to privacy, propensity to take risks, privacy violation experience and trust in institutions, influence Saudi individuals’ perception of sensitivity towards various types of information. The survey results show slight differences in the sensitivity levels between Internet users in Saudi Arabia and their counterparts in other nations. Ultimately, this study contributes to improving the international model of information sensitivity perception between different nations.

Increasing numbers of people fall victim to blackmail on social media. Yet, there has been little attempt to synthesise research on this topic. This study seeks to address this gap by investigating what is currently known about blackmail and the disclosure of sensitive information on social media. Two rapid reviews were conducted and based on their findings those who disclose more information, are younger, female and willing to use social media to create and distribute images are more likely to fall victim to blackmail on social media. However, worry about privacy and the possibility of becoming a victim of blackmail did not necessarily prevent the disclosure of sensitive information on social media. The implications of these findings for interventions and future research are discussed.

Previous contract protocols in blockchains ensure their fairness and traceability by utilizing centralized credible nodes. If credible nodes are dishonest or conspire with the signatory, then other nodes are compromised. Meanwhile, the leakage of sensitive information of participant nodes poses a serious threat to the privacy security of data access in blockchains. To address this issue, this study proposes a secure control method of digital certificate-based data access in blockchains. The proposed method combines blockchain and digital certificate technologies and designs a secure authentication protocol for privacy data in blockchains without verifying the encrypted identity signature of the third-party participant. The high-efficiency network forwarding protocol proposed in this work can support the fair contract signing of multiple signers via blockchain. This protocol can protect the privacy of contracts and identities of participants. Experimental results show that the proposed scheme is superior in terms of communication overhead, storage overhead, and detection rate.

Approximately 90% of the world’s population is involved in some spiritual/religious practice, and this dimension has a relevant role in life. Many studies demonstrate the associations between spirituality/religiosity (S/R), and physical, mental, and social health. Systematic reviews have indicated positive associations, however, the mechanisms behind religious coping are not fully understood. The present study aimed to examine the role of religious affiliation in general (ordinary) and health-related decisions. A nationwide, population-based, cross-sectional study was conducted in Brazil using a self-administered online survey. How much religious affiliation influences decision making was investigated. A total of 1133 participants were included, who were classified as Catholics (43.9%), Evangelicals (18.7%), spiritualists (12.8%), non-religious (11.9%), and others (12.7%). Most participants (66.5%) believed that their religious affiliations had moderate to high influences on their decisions. Participants rated the influence as high in marriage (62.7%), in donations (60.1%), in volunteering (55%), in friendships (53.9%), and in work (50.5%). Concerning health-related decisions, the influence was rated as high in drug use (45.2%), in accepting medical recommendations (45%), and in smoking (43.2%). The influence of religious affiliation on general decision making was significantly correlated for dietary restrictions (r = 0.462), alcohol consumption (r = 0.458), drug use (r = 0.441), tobacco consumption (r = 0.456), and refusal of medical recommendations (r = 0.314). Improving the understanding of how a patient’s beliefs, practices, and experiences affect their health may help healthcare practitioners to take into account religious considerations, not only regarding influences on habits but also regarding adherence to medical treatment advice.

The mining of network sensitive information is of great significance for understanding the social stability of the network. Obtaining the network public opinion of sensitive information is helpful to master Internet users' attitudes toward important social events. The related artificial intelligence technology can achieve the topics from the network texts. At present, the current topic recognition model has a low recognition rate for sensitive information and usually generates some inaccurate topic keywords. In this paper, a topic recognition method of the network sensitive information based on a sensitive word weighted-latent Dirichlet allocation (LDA) model is proposed. First, the basic sensitive word vocabulary is constructed by manual collection, and the embedding representation of the word is obtained through the training of a large amount of network corpus based onWord2vec. The semantic similarity between the word embedding is calculated to extend the basic sensitive word vocabulary. Second, the extended sensitive word vocabulary is embedded in the LDA model. On the one hand, it can improve the semantic understanding and the recognition ability of LDA for the sensitive topic words and promote the quality of the generated topic words. On the other hand, it can also improve the relevance of the topic keywords and the related topics and find more fine-grained keywords. The experimental results show that the sensitive word weighted-LDA model can effectively improve the topic recognition quantity and quality of sensitive information. This paper is helpful to the development of artificial intelligence. The generated corpus in this paper is meaningful to the research of text classification, clustering and information retrieval, and so on.

Social networking and cloud services will collect a large number of high-dimensional and complex data for third party statistical analysis and data mining. Although data analysis is beneficial to users and external parties, they constitute a serious privacy risk disclosure of user sensitive information. First of all, the authors use random forest, decision tree, and support vector machines methods with a discriminant component model to analyze the data set of the new curriculum reform in Shaanxi Province and make an accurate prediction of the impact on bad habits of students’ comprehensive performance. Second, though various kinds of data transformation for privacy protection have been achieved and applied, there is little research designing two preference classification tasks simultaneously. In order to prevent the privacy leakage of sensitive information in the data analysis, the data space projection algorithm based on multiclass discriminant is used to deal with the linear and nonlinear. Clearly, the role of sensitive task and insensitive task can be exchanged. The goal is to preserve the statistical properties of one preference classification as far as possible and to realize the data security of the other preference classification.

Perhaps in the past was difficult to imagine that the moment in which someone could register each purchase that has been made, each book that has been read or each thing that has been said would come. That there would be companies storing data about our physical activity, behaviors, preferences and choices all the time. Most of the personal data comes from acts as daily as installing an application, completing a form, purchasing a product or requesting a service. This information provided sometimes consciously, voluntarily and with relative knowledge of the destination that will have, contrasts with situations in which data are inferred, deduced, extracted and manipulated. In this sense, within a context in which the only access to the database has been left behind to give way to the creation of these by third parties, we wonder about the possible denaturation of the personal data and sensitive information that when get transformed in raw material through the analysis of existent connections and extraction of new data implicit in the multitude of information compiled in public or private databases, would convert people into an essential product for the market; while the development of citizen profiles pay per view would allow predicting behaviors, selections and multiple deployments in different aspects of our lives. It is probable that in the defenselessness condition where we are now for having passed from personal space to social one and from there to the market many times without our knowledge and consent makes necessary to reconsider the current forms of protection and mechanisms of legal protection around our personal data.

2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Exposure of sensitive information can be harmful on its own. In addition, it could enable further attacks. A rigorous and unambiguous definition of information exposure faults can help researchers and practitioners identify them, thus avoiding security failures. This paper describes Information Exposure (IEX), a new class in the Bugs Framework (BF). The IEX class comprises a rigorous definition and (static) attributes of the class, along with their related dynamic properties, such as proximate and secondary causes, consequences and sites. We use the IEX class to analyze specific vulnerabilities and provide clear descriptions. We also discuss lessons we learned that will help create additional BF classes

Big Data promises great socially accepted and desirable benefits. However, in general terms, the datification of life has made people to lose some awareness of the risks involved in the massive analysis of data regarding their fundamental rights. This fact is used by the companies involved in the data value chain to maximize their benefits although this implies the proliferation of negative externalities assumed by the information holders. The Colombian State has made great efforts regarding the protection of data and privacy, as demonstrated by Law 1266 of 2008 and Law 1581 of 2012, nevertheless, a deep literary review leads to conclude the need to adapt to the international context.

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

International audience; Following the introduction of platform-based products, especially considering that platforms are used for multiple brands, there is certainly a growing need for system engineering processes and techniques. This is further emphasized by the fact that companies faced with collaborative platform development frequently need to harmonize often opposing claims from stakeholders with different backgrounds, beliefs, desires and intentions. A core strategy for using resources (e.g., work-hours, knowledge, and production systems) better and more flexibly is to involve suppliers earlier in the development cycle. From this perspective, well-designed and efficiently managed supplier integration is a huge competitive advantage. Supplier integration may range from component design and manufacture to full responsibility for the design of complex distributed systems. The starting point for this work is the results from a previous study, made by the authors, in which a Swedish automotive company and one of its sub-suppliers were examined in order to identify communication barriers. This revealed several problems regarding supplier interaction and information management in projects where both suppliers and product owners contribute their unique knowledge. Following the previous study, the questions to answer include: How can platforms be represented to suit suppliers as well as orginal equipment manufacturers? How does one guarantee efficient, accurate and secure information exchange between the parties involved? Consequently, this article pinpoints some of the problems that companies involved with collaborative product platform development, together with their suppliers, must face today. To answer these questions, interviews, and document studies were conducted for a Swedish truck manufacturer. The results are oriented to the interfaces between product owners and their suppliers.

Data privacy has been studied in the area of statistics (statistical disclosure control) and computer science (privacy preserving data mining and privacy enhancing technologies) for at least 40 years. In this period models, measures, methods, and technologies have been developed to effectively protect the disclosure of sensitive information. The coming of big data, with large volumes of data, dynamic and streaming data, poses new challenges to the field. In this paper we will review some of these challenges and propose some lines of research in the field. Also part of the IFIP AICT Tutorials book sub series (Tutorials, volume 498)

Part 9: Various Aspects of Computer Security; International audience; The mobility of the user and information is a factor that should be taken into account during the design and development of mechanisms protecting the sensitive stored, exchanged and processed information on mobile devices. This paper discusses the security profiles for the user and dispatcher subsystems protecting sensitive information on the mobile device called MobInfoSec. MobInfoSec is a system providing users with secure sensitive documents by using the specialized class SP cryptographic module, which protects directly the trusted system components through implementing ORCON access control rules. Protection Profile defines the security functional requirements for MobInfoSec system executing the encryption/decryption of documents based on addressed access policies. The article includes a general description of MobInfoSec system, including assets, assumptions, threats, policies and functional requirements necessary for the evaluation of security functions developed in accordance with requirements of the standard ISO/IEC 15408 (called the Common Criteria).