DBSDS: A dual‐blockchain security data sharing model with supervision and privacy‐protection

In the era of big data, international trade has gone through a digital transformation and developed into a new stage of digital trade. The gradual prosperity of digital trade and the increasing contribution of digital elements to the economy of countries and the international community also bring about problems such as data abuse, surveillance, privacy infringement, and regulatory difficulties. In order to adapt to the development trend of digital trade, countries are paying more attention to cross-border data flow and data privacy protection, in order to maintain national security and the data security of various entities. Therefore, this article analyzes the current situation and trends of digital trade, considers the security issues of data privacy from the perspective of different entities, links key issues such as cross-border data flow, data rights, and privacy rights, explores the current status of relevant domestic and foreign legal and regulatory systems, and conducts comparative analysis. The analysis results show that due to different values, there are significant differences in policies regarding digital trade and privacy protection among countries. The commonality is that none of them have established appropriate legal protection systems for data elements and privacy rights to promote the development of digital trade. Under the trend of technological development, data privacy will also be impacted by new technologies through digital trade. Therefore, in response to the expanding demand for data regulation, all entities should improve their privacy protection and cross-border data flow policies and standards from both internal and international perspectives.

With the rapid development of big data technology, its application in the field of new media is becoming more and more extensive, especially in personalized service shows great potential. This paper discusses in depth the application, challenges and future development of big data technology in new media personalized service. The following conclusions are drawn: (1) Big data technology plays a key role in new media personalized service Big data technology can construct a refined user profile, realize personalized content recommendation, and improve user satisfaction and loyalty by collecting, analyzing and utilizing user behavioral data on new media platforms. (2) Big data technology faces multiple challenges in new media personalized services. Although big data technology shows great potential in new media personalized service, it still faces challenges in data privacy and security, data quality and accuracy, technology update and algorithm optimization, as well as regulatory compliance and ethics. (3) Innovative applications promote the development of big data technology in new media personalized services. With the continuous innovation and application of big data technology, new media personalized services are also developing and improving.

Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.

With the fast development of big data technology, its application in many fields is becoming more common. Big data has played an essential role in public health, and the influence of data usage expands with the rising of technology. This paper would give a brief overview of the current application of big data, specifically in the context of the COVID-19 pandemic and discuss the current issues and problems of data usage in public health related to privacy concerns. While Big Data is looking to be beneficial in many areas, especially in public health, better regulations to ensure data safety and privacy are urgently needed.KeywordsBig dataTechnologyPublic healthCollect data

E-governance can be implemented very effectively using the cloud computing technologies. Security and privacy of data are the major challenges in cloud-based e-governance systems. With proper access control and user revocation schemes, we can protect the privacy and security of data stored in cloud servers used for e-governance. There are a lot of techniques to achieve the controlled access in cloud computing. In some applications of e-government, the users should be allowed to access data, only for a specific period of time. In this paper, we introduce a temporal access control scheme based on attribute-based encryption (ABE), which allows users to access the data according to their access policy and for a specific time period only. Our model includes both the temporal access scheme and a time-based proxy re-encryption scheme to allow a user's access right to expire automatically.

BTMDS: Blockchain trusted medical data sharing scheme with privacy protection and access control

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

In view of the wide application and popularization of large data, more and more data security and privacy issues have brought great challenges to the development of large data. Starting from the characteristics of big data, this paper analyses various risks of information security, and puts forward the corresponding development strategy of big data security. The results show that the combination of technology and relevant policies and regulations can better solve the problem of big data security and privacy protection.

Cloud computing provides an effective way to manage and share massive medical data for Electronic Medical Record (EMR), hence establishing cloud-based EMR has attracted more and more attention. The data integrity and privacy issue in cloud-based EMR should be emphasized since users do not want their medical records to be damaged or disclosed to others. To address the concern in this paper, we propose an efficient and secure Data Integrity Auditing scheme with Traceability for cloud-based EMR (DIAT), which provides data integrity and privacy. We store multiple copies so that data can be recovered quickly from damage as long as one copy remains intact; meanwhile, data cannot be leaked to unauthorized entities since it is stored as ciphertext. For supporting data dynamics, we have designed a two-dimensional data structure, called Dynamic Mapping Hash Table (DMHT). It does not require large auxiliary validation information, nor does it affect the sequence numbers of other blocks. Moreover, data traceability is achieved by organizing all versions of a data block as a chain so that doctors are enabled to track the changes of patients condition in their records. In addition, formal security analysis and experiment results confirm that DIAT is provably secure and efficient.

This chapter analyzes the various types of policies implemented by the Web security services. According to X.800 definition, there are five basic Web security services categories: authentication, non-repudiation, access control, data integrity, and data confidentiality. In this chapter, we discuss access control and data privacy services. Access control services may adopt various models according to the needs of the protected environment. In order to guide the design of access control models, several policy-expressing languages have been standardized. Our contribution is to describe and compare the various models and languages. Data privacy policies are categorized according to their purpose, that is, whether they express promises and preferences, manage the dissemination of privacy preferences, or handle the fulfillment of the privacy promises. The chapter is enriched with a discussion on the future trends in access control and data privacy.

Ciphertext policy attribute-based encryption (CP-ABE) is a promising cryptographic technology and a key component that enable secure data sharing in a cloud environment through fine-grained access control. Since it was introduced, many interesting schemes have been proposed. However, in addition to managing data sharing through access control, a comprehensive scheme should also cater for user revocation and ciphertext queries. This is because in a cloud environment new users may join while existing users may leave the system. At the same time, given the potentially large amount of data stored in a cloud storage, user should be able to retrieve the required files efficiently in a privacy-preserving manner. To address the above issue, in this paper, we propose a practical searchable CP-ABE scheme supporting user revocation. In contrast to existing schemes that provide only single keyword query, our efficient search function provides conjunctive search, which allows user to locate a ciphertext related to a set of keywords. The computation overhead of our user revocation is at least on par with existing schemes. Besides, the security analysis indicates that the proposed scheme is secure under the decisional Bilinear Diffie-Hellman assumption. We also provide extensive experimental results to confirm the efficiency and feasibility of our proposed construction.

As the main strategic resource in the field of education, big data in the field of education provides a scientific basis for educational decision-making by managers and serves as a huge driving force for innovation and change in the field of education. At the same time, in the process of sharing and analyzing large educational data, data privacy inevitably creates many obstacles to the application and development of educational data. This article focuses on the study of the content, structure and technology of educational big data privacy protection in order to provide support for the privacy protection mechanism and technical support for the effective application of educational big data.

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.