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Showing papers on "Data access published in 2022"


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an auditable access control model, based on an attribute-based access control, and managed the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network.
Abstract: Internet of Things (IoT) devices are widely considered in smart cities, intelligent medicine, and intelligent transportation, among other fields that facilitate people's lives, producing a large amount of private data. However, due to the mobility, limited performance, and distributed deployment of IoT, traditional access control methods cannot support the security of private data's access control process in current IoT environments. To address such problems, this article proposes an auditable access control model, based on an attribute-based access control model, and manages the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network. Additionally, a Blockchain-based auditable access control system is also proposed based on the auditable access control model, ensuring private data security in IoT environments and realizing effective management and auditable access to these data. Experimental results show that the proposed system can maintain high throughput while ensuring private data security for real application scenarios in IoT environments.

43 citations


Journal ArticleDOI
TL;DR: All data policies need to address data access and preservation, and an open data regime not only maximizes the benefit of the data, it also simplifies most of the other issues around effective research data stewardship and infrastructure development.
Abstract: The first purpose of data policy should be to serve the objectives of the organization or project sponsoring the collection of the data. With research data, data policy should also serve the broader goals of advancing scientific and scholarly inquiry and society at large. This is especially true with government-funded data, which likely comprise the vast majority of research data. Data policy should address multiple issues, depending on the nature and objectives of the data. These issues include data access requirements, data preservation and stewardship requirements, standards and compliance mechanisms, data security issues, privacy and ethical concerns, and potentially even specific collection protocols and defined data flows. The specifics of different policies can vary dramatically, but all data policies need to address data access and preservation. Research data gain value with use and must therefore be accessible and preserved for future access. This article focuses on data access. While policy might address multiple issues, at a first level it must address where the data stand on what Lyon (2009) calls the continuum of openness. Making data as openly accessible as possible provides the greatest societal benefit, and a central purpose of data policy is to work toward ethically open data access. An open data regime not only maximizes the benefit of the data, it also simplifies most of the other issues around effective research data stewardship and infrastructure development.

34 citations


Proceedings ArticleDOI
10 Jun 2022
TL;DR: The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregation data centers.
Abstract: Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization prin- ciples for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers.

15 citations


Journal ArticleDOI
01 Jan 2022-Sensors
TL;DR:
Abstract: The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN.

14 citations


Book ChapterDOI
01 Jan 2022
TL;DR: There is a need to propose a verified algorithm that is efficient in retrieving pages from Google using Deep Learning to increase retrieval efficiency of web pages as per the user’s requirement from the big data.
Abstract: With the advancement of search engines, a major change has occurred in the way people are accessing data on the net. Search engines have made access to data efficient and easier as billions of pages on the net (or called big data) are suggested at once. The pages with the most significant rank generally have a higher visibility rate to people and hence every webmaster wants to push their page to higher rank. As a result, Search Engine Optimization (SEO) has become a massive business which strives in enhancing the ranking of clients’ webpage. But there are many myths and misconceptions about the ranking algorithms due to inadequate knowledge about SEO’s methods. Still there is a need to propose a verified algorithm that is efficient in retrieving pages from Google. The link analysis algorithm is in accordance with the link structure of any document as the page which has many links also has many connections to it and hence can increase retrieval capacity. There is another approach called the integrated ranking approach which comes under personalized web research. In the integrated ranking approach, both content and link are used as parameters to improve retrieval efficiency. This approach is used by Google using Deep Learning to increase retrieval efficiency of web pages as per the user’s requirement from the big data.

14 citations


Journal ArticleDOI
01 Feb 2022
TL;DR: The research progress on multisource heterogeneous urban sensor access and data management technologies provide strong support for intelligent perception and scientific management at the city scale and can accelerate the construction of smart cities or digital twin cities with virtual reality features.
Abstract: Urban sensors are an important part of urban infrastructures and are usually heterogeneous. Urban sensors with different uses vary greatly in hardware structure, communication protocols, data formats, interaction modes, sampling frequencies, data accuracy and service quality, thus posing an enormous challenge to the unified integration and sharing of massive sensor information resources. Consequently, access and data management methods for these multisource heterogeneous urban sensors are extremely important. Additionally, multisource heterogeneous urban sensor access and data management technologies provide strong support for intelligent perception and scientific management at the city scale and can accelerate the construction of smart cities or digital twin cities with virtual reality features. We systematically summarize the related research on these technologies. First, we present a summary of the concepts and applications of urban sensors. Then, the research progress on multisource heterogeneous urban sensor access technologies is analysed in relation to communication protocols, data transmission formats, access standards, access technologies and data transmission technologies. Subsequently, the data management technologies for urban sensors are reviewed from the perspectives of data cleaning, data compression, data storage, data indexing and data querying. In addition, the challenges faced by the technologies above and corresponding feasible solutions are discussed from three aspects, namely, the integration of massive Internet of Things (IoT), computational burden and energy consumption and cybersecurity. Finally, a summary of this paper is given, and possible future development directions are analysed and discussed.

10 citations


Journal ArticleDOI
TL;DR: Harmony across Europe of expanded access regulations could reduce manufacturer burdens, improve patient access, and yield better data, and changes would better balance the need to generate quality evidence with the desire for pre-approval access to investigational medicine.
Abstract: Patients with rare diseases often have limited or no options for approved treatments or participation in clinical trials. In such cases, expanded access (or “compassionate use”) provides a potential means of accessing unapproved investigational medicines. It is also possible to capture and analyze clinical data from such use, but doing so is controversial. In this perspective, we offer examples of evidence derived from expanded access programs for rare diseases to illustrate its potential value to the decision-making of regulators and payers in the European Union and the United States. We discuss ethical and regulatory aspects to the use of expanded access data, with a focus on rare disease medicines. The heterogeneous approach to expanded access among countries within the European Union leaves uncertainties to what extent data can be collected and analyzed. We recommend the issuance of new guidance on data collection during expanded access, harmonization of European pathways, and an update of existing European compassionate use guidance. We hereby aim to clarify the supportive role of expanded access in evidence generation. Harmonization across Europe of expanded access regulations could reduce manufacturer burdens, improve patient access, and yield better data. These changes would better balance the need to generate quality evidence with the desire for pre-approval access to investigational medicine.

8 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors designed a new cryptographic primitive (i.e., TFPRE-OT) by exploiting type-based proxy re-encryption for fine-grained sharing and oblivious transfer for hiding the access histories of data requesters.

8 citations


DOI
01 Jan 2022
TL;DR: In this paper, the authors have reviewed the different types of SQL injection attacks and existing techniques for the detection of SQL Injection attacks and have analyzed the performance of Machine learning algorithms like Naive Bayes, Decision trees, Support Vector Machine, and K-nearest neighbor.
Abstract: SQL Injection attacks are one of the major attacks targeting web applications as reported by OWASP. SQL injection, frequently referred to as SQLI, is an arising attack vector that uses malicious SQL code for unauthorized access to data. This can leave the system vulnerable and can result in severe loss of data. In this research work, we have reviewed the different types of SQL Injection attacks and existing techniques for the detection of SQL injection attacks. We have compiled and prepared our own dataset for the study including all major types of SQL attacks and have analyzed the performance of Machine learning algorithms like Naive Bayes, Decision trees, Support Vector Machine, and K-nearest neighbor. We have also analyzed the performance of Convolutional Neural Networks (CNN) on the dataset using performance measures like accuracy, precision, Recall, and area of the ROC curve. Our experiments indicate that CNN outperforms other algorithms in accuracy, precision, recall, and area of the ROC curve.

8 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors provided an overview of major data sources in China that can be potentially used for epidemiology, health economics, and outcomes research; compare them with similar datasets in other countries; and discuss future directions of healthcare data development in China.
Abstract: Objectives This study aimed to provide an overview of major data sources in China that can be potentially used for epidemiology, health economics, and outcomes research; compare them with similar data sources in other countries; and discuss future directions of healthcare data development in China. Methods The study was conducted in 2 phases. First, various data sources were identified through a targeted literature review and recommendations by experts. Second, an in-depth assessment was conducted to evaluate the strengths and limitations of administrative claims and electronic health record data, which were further compared with similar data sources in developed countries. Results Secondary databases, including administrative claims and electronic health records, are the major types of real-world data in China. There are substantial variations in available data elements even within the same type of databases. Compared with similar databases in developed countries, the secondary databases in China have some general limitations such as variations in data quality, unclear data usage mechanism, and lack of longitudinal follow-up data. In contrast, the large sample size and the potential to collect additional data based on research needs present opportunities to further improve real-world data in China. Conclusions Although healthcare data have expanded substantially in China, high-quality real-world evidence that can be used to facilitate decision making remains limited in China. To support the generation of real-world evidence, 2 fundamental issues in existing databases need to be addressed—data access/sharing and data quality.

6 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed a data replica creation scheme based on a Level of Privacy (LoP) defined by data owners and the service capacity of fog nodes, which can significantly achieve efficient replicas privacy, prediction accuracy, and outperform the existing state-of-the-art schemes in terms of computational and memory costs.

Journal ArticleDOI
TL;DR: Kokosi and Harron as discussed by the authors describe synthetic data as "artificial data that can be used to support efficient medical and healthcare research, while minimising the need to access personal data".
Abstract: ⇒ Synthetic data are artificial data that can be used to support efficient medical and healthcare research, while minimising the need to access personal data ⇒ More research is needed to determine the extent to which synthetic data can be relied on for formal analysis, the cost effectiveness of generating synthetic data, and how to accurately assess disclosure risk Synthetic data have the potential to improve medical research while minimising the need to access personal data; Theodora Kokosi and Katie Harron explain what they are and how they are used.

Journal ArticleDOI
TL;DR: In this article , an attribute-based access control framework is proposed aiming to provide prompt and secure access to medical data globally by utilizing state-of-the-art technologies and standards, including Next-Generation Access Control (NGAC), blockchain and smart contracts.
Abstract: The COVID-19 pandemic further outlined the importance of global healthcare services provisioning for diagnosing and treating patients who tend to travel and live for large periods away from home and can be anywhere at any given time. Advances in technology enable healthcare practitioners to access critical data regarding a person’s health status to provide better services. Medical data are sensitive in nature, and therefore, a reliable mechanism should ensure that only authorized entities can access data when needed. This paper, through a layered consideration of a Globalized Healthcare Provisioning Ecosystem (GHPE), reveals the interdependencies among its major components and suggests a necessary abstraction to identify requirements for the design of an access control suitable for the ecosystem. These requirements are imposed by the nature of the medical data as well as by the newly introduced potentials of Internet of Medical Things (IoMT) devices. As a result, an attribute-based access control framework is proposed aiming to provide prompt and secure access to medical data globally by utilizing state-of-the-art technologies and standards, including Next-Generation Access Control (NGAC), blockchain and smart contracts. Three types of smart contracts are proposed that enable access control to implement attribute and policy stores where policy classes and attributes are decentralized and immutable. In addition, the usage of blockchain-based distributed identities allows patients to be in control of access to their medical data and also enables healthcare service providers to access medical data promptly and reliably through the proposed access control framework. The qualitative characteristics of the proposed approach toward a decentralized and patient-centric access control in GHPE are demonstrated and discussed based on an application paradigm.

Journal ArticleDOI
TL;DR: Genomics4RD is introduced, an integrated web‐accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs.
Abstract: Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R‐SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web‐accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

Journal ArticleDOI
TL;DR: In this article , the authors presented a summary of the concepts and applications of urban sensors and analyzed the research progress on multisource heterogeneous urban sensor access technologies in relation to communication protocols, data transmission formats, access standards, access technologies and data transmission technologies.

Journal ArticleDOI
01 Feb 2022-Heliyon
TL;DR: In this article , the authors discuss the challenges of secure data access in big data, present some of the frameworks and techniques and conclude with recommendations for secure access of big data. But they do not consider the privacy concerns of data.

Proceedings ArticleDOI
23 Feb 2022
TL;DR: A security sharing method for IoV data is presented that utilizes blockchain technology and weighted cipher text policy attribute-based encryption to address the issues that traditional Internet of Vehicles data is readily manipulated with and access control is inflexible.
Abstract: To address the issues that traditional Internet of Vehicles (IoV) data is readily manipulated with and access control is inflexible, a security sharing method for IoV data is presented that utilizes blockchain technology and weighted cipher text policy attribute-based encryption. Maintain the production, verification, and storage of blocks, implement distributed data storage, and secure the integrity of data; access control of data on the chain based on characteristics to ensure that only authorized visitors may access data content; For the Internet of Vehicles data access, a multi-attribute-based hierarchical access policy formulation approach is built by mining the association relationship between points and permissions between roles. This method simplifies the complexity of access control policies. The built-in hierarchical access policy formulation approach efficiently reduces the computation and transmission overhead of cars while meeting the needs of the Internet of Vehicles scenario for access to various entities and different roles.

Journal ArticleDOI
04 Nov 2022-Systems
TL;DR: In this article , a fine-grained access control (FGAC) framework for supply chain data sharing is proposed, based on the blockchain Hyperledger Fabric, which augments role-based access control by giving different attribute keywords to different types of users.
Abstract: With the rapid development of digital economics, a large number of data have been accumulated in the supply chain system, and data islands have appeared. Data sharing is an imperative way to unlock the data value of a supply chain system. A safe and effective access control mechanism for privacy-sensitive data is key in data sharing. At present, traditional access control mechanisms are static, single-factor control, and prone to a single point of failure. For dealing with these, a fine-grained access control (FGAC) framework for supply chain data sharing is proposed, based on the blockchain Hyperledger Fabric. It augments role-based access control (RBAC) by giving different attribute keywords to different types of users. This framework is implemented in smart contract Chaincodes and quantitatively verified by using the model-checking tool UPPAAL. The experiment results show that the FGAC framework enhances the efficiency and safety in the process of data sharing for the supply chain system, compared with the existing works.

Proceedings ArticleDOI
06 Sep 2022
TL;DR:
Abstract: Exploratory efforts in mobile health (mHealth) data collection and sharing have achieved promising results. However, fine-grained contextual access control and real-time data sharing are two of the remaining challenges in enabling temporally-precise mHealth intervention. We have developed an NDN-based system called mGuard to address these challenges. mGuard provides a pub-sub API to let users subscribe to real-time mHealth data streams, and uses name-based access control policies and key-policy attribute-based encryption to grant fine-grained data access to authorized users based on contextual information. We evaluate mGuard's performance using sample data from the MD2K project.

Journal ArticleDOI
TL;DR: The AMUNATCOLL IT system as mentioned in this paper enables access to and exploration and manipulation of data available in the database containing unique natural collections from the Faculty of Biology of Adam Mickiewicz University in Poznań (FBAMU).
Abstract: Abstract The paper describes the interfaces implemented in the AMUNATCOLL IT system, which enable access to and explorationand manipulation of data available in the database containing unique natural collections from the Faculty of Biology of Adam Mickiewicz University in Poznań (FBAMU). Data can be accessed using the two available interfaces: graphical and programming application interfaces. The first is implemented in two forms: a portal, which is the main interface for accessingthe data stored in the database, and a mobile application that complements functions related to field research and creating private collections. To deliver the required set of operations, the portal was equipped with simplified and advanced searching, statistical analysis and spatial processing (BioGIS). Data openness and the ability to collaborate with other solutions and systems are key elements in achieving synergies in conducting research on biodiversity. AMUNATCOLL IT offers an opportunity to respond to these challenges, enabling data export for independent processing with external tools related to portal functionality or giving access to data directly using an application programming interface. Graphical interfaces are subject to numerous requirements and restrictions reflected in the graphic design and accessibility issues related to the accommodation of disabled individuals. These interfaces must properly address both groups of target recipients, considering their different goals and level of knowledge, as well as adjusting the level of interaction due to the limitations of using the interface.

Journal ArticleDOI
TL;DR: It is argued that improved auditability, consistency, and efficiency of the data access request process using ADS systems have the potential to yield fairer outcomes in requests for data largely sourced from biospecimens and biobanked samples.
Abstract: Studies on the ethics of automating clinical or research decision making using artificial intelligence and other algorithmic tools abound. Less attention has been paid, however, to the scope for, and ethics of, automating decision making within regulatory apparatuses governing the access, use, and exchange of data involving humans for research. In this article, we map how the binary logic flows and real-time capabilities of automated decision support (ADS) systems may be leveraged to accelerate one rate-limiting step in scientific discovery: data access management. We contend that improved auditability, consistency, and efficiency of the data access request process using ADS systems have the potential to yield fairer outcomes in requests for data largely sourced from biospecimens and biobanked samples. This procedural justice rationale reinforces a broader set of participant and data subject rights that data access committees (DACs) indirectly protect. DACs protect the rights of citizens to benefit from science by bringing researchers closer to the data they need to advance that science. DACs also protect the informational dignities of individuals and communities by ensuring the data being accessed are used in ways consistent with participant values. We discuss the development of the Global Alliance for Genomics and Health Data Use Ontology standard as a test case of ADS for genomic data access management specifically, and we synthesize relevant ethical, legal, and social challenges to its implementation in practice. We conclude with an agenda of future research needed to thoughtfully advance strategies for computational governance that endeavor to instill public trust in, and maximize the scientific value of, health-related human data across data types, environments, and user communities.

Journal ArticleDOI
TL;DR: In this article, the authors describe enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 2.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor.
Abstract: As low-cost air quality sensors become more widely utilized, more tools and methods are needed to help users access/process sensor data, identify poorly performing sensors, and analyze/visualize sensor data. Free and open-source software (FOSS) packages developed for use on FOSS data science platforms are well-suited to support this need by offering replicable and shareable tools that can be adapted to meet a user or project's specific needs. This paper describes enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 1.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor. This paper also demonstrates how these enhancements may be used to track and assess the health of air sensors in real-time or for large historical datasets. The dataset used for this analysis was collected during a multi-year project (with sensors deployed from October 2017 to October 2020) involving the distribution of approximately 400 PA-II sensors across 14 communities in southern, central, and northern California. Applying the tools in the AirSensor package revealed a dramatic variability in sensor performance, mainly driven by seasonal trends or particulate matter source type. These results also indicate that this sensor can provide useful data for at least three years with little evidence of substantial or consistent drift. Further, high agreement was observed between co-located sensors deployed at different times, indicating that it may be reasonable to compare data from old and new PA-II sensors. In addition to assessing the long-term performance and reliability of the PA-II sensor, this analysis serves as a model for how data from large sensor networks may be effectively processed, evaluated, interpreted, and communicated.


Journal ArticleDOI
TL;DR: CapBlock is a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts to manage the access to information in federated IoT environments.
Abstract: The increase in the interconnection of physical devices and the emergence of the 5 G paradigm foster the generation and distribution of massive amounts of data. The complexity associated with the management of these data requires a suitable access control approach that empowers citizens to control how their data are shared, so potential privacy issues can be mitigated. While well-known access control models are widely used in web and cloud scenarios, the IoT ecosystem needs to address the requirements of lightness, decentralization, and scalability to control the access to data generated by a huge number of heterogeneous devices. This work proposes CapBlock, a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts. CapBlock is intended to manage the access to information in federated IoT environments where data need to be managed through access control policies defined by different data providers. The feasibility of CapBlock has been successfully evaluated in the scope of the EU research project IoTCrawler, which aims at building a secure search engine for IoT data in large-scale scenarios.

Journal ArticleDOI
TL;DR: In this article , the authors analyze the scope of data covered by Art. 20 GDPR, the conditions of its execution, and its practicality with respect to the transaction costs involved.
Abstract: Restrictions of data access for complementary services in digital (IoT) ecosystems are an increasing concern in the legal and economic discussion around the interface between competition law and data protection. The connected car and its ecosystem of innovative complementary products and services is exemplary for this problem. Car manufacturers (OEMs) enjoy exclusive control over most in-vehicle data and thus a gatekeeper position that allows them to control complementary markets. One of a number of potential solutions to this problem is the application of the right to data portability of the General Data Protection Regulation (GDPR). This paper shows the difficulties of solving this data access problem through Art. 20 GDPR. In particular, we analyze the scope of data covered by Art. 20 GDPR, the conditions of its execution, and its practicality with respect to the transaction costs involved. Our findings suggest that Art. 20 GDPR is insufficient to solve the data access problem in the ecosystem of connected cars. Key Words: Data Portability | Data Access | Data Protection Law | Competition Policy | Connected Cars | PSD2 | Consumer Data Rights

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an efficient and secure multi-owner access control scheme (ESMAC) for access authorization in multi-level data processing (MLDP) scenario, where data are processed by a series of parties who also insert new data.
Abstract: Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.

Journal ArticleDOI
TL;DR: In this paper , the authors used paradox theory to address the complex and multi-faceted phenomenon of data access in digital servitization and provided a comprehensive set of coping strategies.

Journal ArticleDOI
TL;DR: This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community to enable the multiple (re)use of data with secure access functionality by clinicians (patient care).
Abstract: Abstract The Virus Outbreak Data Network (VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable (FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval (CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple (re)use of data with secure access functionality by clinicians (patient care), an idea aligned with the FAIR-based Personal Health Train (PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace (to produce metadata) or dashboard (for the patient) must be clear to design the IT architecture. This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.

Proceedings ArticleDOI
07 Feb 2022
TL;DR: Overall, the results show that data access refactorings focus on improving the code quality but not the underlying data access operations, which indicates that more work is needed from the research community on providing awareness and support to practitioners on the benefits of addressing data access smells with refactoring.
Abstract: Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. However, refactoring in data-intensive systems is not well explored. Understanding the refactoring practices of developers is important to develop efficient tool support. We conducted a longitudinal study of refactoring activities in data access classes using 12 data-intensive subject systems. We investigated the prevalence and evolution of refactorings and the association of refactorings with data access smells. We also conducted a manual analysis of over 378 samples of data access refactoring instances to identify the functionalities of the code that are targeted by such refactorings. Our results show that (1) data access refactorings are prevalent and different in type. Rename variable is the most prevalent data access refactoring. (2) The prevalence and type of refactorings vary as systems evolve in time. (3) Most data access refactorings target codes that implement data fetching and insertion. (4) Data access refactorings do not generally touch SQL queries. Overall, the results show that data access refactorings focus on improving the code quality but not the underlying data access operations. Hence, more work is needed from the research community on providing awareness and support to practitioners on the benefits of addressing data access smells with refactorings.

Journal ArticleDOI
TL;DR: In this article , the authors examine the co-constitutive links between policy, design, and practice of Data Subject Access Requests (DSARs) and find that the DSAR process was not linear and participants employed many work-arounds.
Abstract: The right of access, found in the EU's GDPR and similar data protection regulations around the world, requires corporations and other organizations to give people access to the data they hold about them. Such regulations create obligations for data controllers but leave flexibility on how to achieve them, resulting in variation in how Data Subject Access Requests (DSARs) are implemented by different corporations. To understand the various practices emerging around DSARs and how requesting data influences the way people think of data protection laws, we asked participants in India, the UK and USA to make 38 DSARs from 11 different companies. Using the metaphor of the policy-design-practice "knot" ~\citejacksonPolicyKnotReintegrating2014, we examine DSARs as a case of the co-constitutive links between policy, design, and practice. We find that the DSAR process was not linear and participants employed many work-arounds. The challenges they encountered in the overall DSAR process negatively affected their perceptions of data protection policies. Our study suggests that researchers have to be flexible in adapting research methodology to understanding emerging practices, and that there is a need for more collaborative experimentation with DSARs before standardizing the process.