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In the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity.

A new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system.

In the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification.

The IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.

In traditional commerce, an auction is known as a mechanism of determining the value of a commodity that does not have a fixed price. Auctions are exciting and an increasing number of transactions are performed through e‐auctions. But most current auctions cannot address all the important security requirements. Usually, auction systems force bidders and sellers to trust the auctioneer and, on the other hand, do not provide anonymity for bidders and sellers. This paper aims to solve these problems by presenting an efficient anonymous secure auction schema (ASAS) without a fully trustworthy auctioneer.

The paper analyzes security properties and the complexity of previous works in auction security and then proposes a new ASAS that is more secure and efficient than previous works. Finally, security properties and the complexity of the new schema and previous works are compared with one another.

The proposed auction protocol does not force bidders and sellers to trust the auctioneer. In addition, it provides anonymity for both of them. Owing to these newly added features and high degree of security of ASAS, it is suggested that its use in high‐value auctions should require tighter security.

The paper proposes a new schema for electronic auctions that is secure and efficient and, in addition, does not force bidders and sellers to trust the auctioneer.

This paper aims to introduce a systematic computing and analytical procedure that is applied to the co-author network to identify the temporal evolution and growth of research collaborations in the area of e-governance. The empirical analysis of the temporal co-author network can trace the emerging authors and knowledge bursts over time.

The study applied social network theory to trace the author collaboration patterns in the domain of e-governance. Analysis of the co-author network using micro and macro parameters was done to trace the temporal evolution of the author collaborations.

E-governance is a multi-disciplinary research domain split over streams of management, politics, information technology and electronics. Hence, research collaborations play a significant role in its advancement. The knowledge sharing between individual authors, institutions and groups through research collaborations, resulting in extensive sharing of data, equipment and research methods, has boosted research activities and development in e-governance. In this paper, the authors systematically analyse the current scenario of research collaborations in the area of e-governance using co-author network to estimate its impact on the advancement of the field. The authors also analysed the temporal evolution of the co-author networks, which show remarkable growth of research collaborations in the domain of e-governance from the year 2000.

The co-author network analysis is only a proxy measure for the analysis of research collaborations. The names of the authors and the university affiliations used in the article are as retrieved from the research repository of Scopus. The degree, citations and other parameters related with authors have scope only within the environment of the co-author network used in the analysis. The criteria used in the study is limited to the degree of research collaborations and the number of co-authored publications in the giant component of the co-author network.

Institutions, authors and governments can trace and select suitable topics and choose research groups of co-authors over the world for future research collaborations in e-governance. The knowledge about the emerging and most discussed topics gives an overview of the global research trends of e-governance.

The study identified the evolution of creative collaborations in e-governance in the global perspective. The methodology introduced here is helpful to detect the proficient and productive author collaborations and the spectrum of related e-governance research topics associated with them. As the author collaborations can be mapped to the institutional and country-level collaborations, the information is helpful for researchers, institutions and governments to establish the best collaborations in e-governance research based on the author proficiency, collaboration patterns and research topics as per the requirements.

The paper introduces a novel research methodology using temporal analysis of co-author network to identify the evolution of research patterns and the associated research topics.

Currently, electronic election is one of the most popular issues of e-democracy. This has led to the development of applications and several security mechanisms to address such necessity. The problem that arises is that such applications are created either on demand for a specific election process, or experimentally for scientific purposes. The purpose of this study is to present a new e-voting system, called VOTAN. The VOTAN system involves a combination of new features with basic advantages, the implementation as open source software, its modular organization covering the functional requirements of a typical electronic voting system (EVS) and the capability of data analysis of candidates and voters.

VOTAN stands for VOTes Analyzer. It is a secure application for the conduct of electronic elections through the internet based on its own security protocol. It also includes a data analysis component which analyzes the election results and investigates the factors that play a crucial role. The major advantages of the system are that it is an open source and includes a data analysis module that can distinguish important variables from the elections and help make predictions for the outcome based on the selected variables. It is a practical solution to the existing e-voting applications and is ideal for small communities such as organizations, universities and chambers.

Its main advantage, compared to similar e-voting systems, is the integration of the data analysis component. The analysis of the data produced from elections is considered a critical process to fully comprehend the outcome of the elections and its correlation to specific attributes/variables of the election process. The data analysis module is a unique feature of VOTAN. It facilitates the selection of the most important attributes that influence the outcome of elections and creates a mathematical model to predict the outcome of an election based on the selected attributes. The method used in the module is the LDA.

The originality of the paper derives from the data analysis component and its security protocol/schema that fulfils several requirements.

Presently, existing electric car sharing platforms are based on a centralized architecture which are faced with inadequate trust and pricing issues as these platforms requires an intermediary to maintain users’ data and handle transactions between participants. Therefore, this article aims to develop a decentralized peer-to-peer electric car sharing prototype framework that offers trustable and cost transparency.

This study employs a systematic review and data were collected from the literature and existing technical report documents after which content analysis is carried out to identify current problems and state-of-the-art electric car sharing. A use case scenario was then presented to preliminarily validate and show how the developed prototype framework addresses the trust-lessness in electric car sharing via distributed ledger technologies (DLTs).

Findings from this study present a use case scenario that depicts how businesses can design and implement a distributed peer-to-peer electric car sharing platforms based on IOTA technology, smart contracts and IOTA eWallet. Main findings from this study unlock the tremendous potential of DLT to foster sustainable road transportation. By employing a token-based approach this study enables electric car sharing that promotes sustainable road transportation.

Practically the developed decentralized prototype framework provides improved cost transparency and fairness guarantees as it is not based on a centralized price management system. The DLT based decentralized prototype framework aids to orchestrate the incentivize monetization and rewarding mechanisms among participants that share their electric cars enabling them to collaborate towards lessening CO₂ emissions.

The findings advocate that electric vehicle sharing has become an essential component of sustainable road transportation by increasing electric car utilization and decreasing the number of vehicles on the road.

The key novelty of the article is introducing a decentralized prototype framework to be employed to develop an electric car sharing solution without a central control or governance, which improves cost transparency. As compared to prior centralized platforms, the prototype framework employs IOTA technology smart contracts and IOTA eWallet to improve mobility related services.

This work reports on a developing method time measurement system for measuring manufacturing and assembly processes automatically. This automatic system enables the production engineers and management to detect, process, and display concise and accurate information about the operations in real time.

This system is based on Internet of things technology and RFID-antenna. This methodology consists of seven main steps and one final optimization step. Mainly, the operator is equipped by RFID reader, and the work station tools and devices are provided by RFID tags. Responding the RFID tags to the reader will refer to the certain operations, the difference time between start and end of the operations will be collected immediately and calculated by the microprocessor of the system.

This automatic system is promising, considering the accurate time measurements and recommendations that obtained from the case study which includes measuring manual assembly operations to be followed in order to overcome the limitations which are not only technical but also managerial, legal and organizational.

The acquired data about timing and duration of individual operations are anonymized to guarantee the compliance with respect to the privacy laws (GDPR and Italian work's laws).

This work presents a unique system to measure the time instead of traditional methods in the factories environment and satisfies the requirements to study the recommendations in order to overcome the challenges.

This paper aims to propose a framework for how privacy-preserving technologies (PETs) create business value for organizations. The framework was developed by examining the literature on privacy and information technology’s impact (symbolic and function). The authors evaluate the framework’s applicability using multiparty computation (MPC) as an instance of PETs, with expert interviews in the telecommunication industry.

In an illustrative case of four telecommunication companies, the authors conducted semi-structured interviews with experts and used MPC as an instance of PET.

The evaluation of the framework indicates that PETs create business value for organizations: enhancing customer interactions, sales, personalized services, predicting market trends and collaboration among organizations. The findings show that business value of PETs is mainly driven by consumers and organizations willing to share data and collaborate.

This study was limited to the telecom sector and focused on MPC as an instance of PET. Further studies should be conducted to explore the benefits of other PETs and MPC. Future research could find out if this framework is also helpful for implementing other PETs or even other types of technology. The authors’ framework provides factors that future studies can use to quantify the impact of PETs. The authors hope that this framework provides an overarching reference for organizations considering the adoption of PETs.

The authors’ findings inform managers in exploring the business value of PETs for organizations. This study also provides insights into which costs and risks to consider when implementing PETs.

This study is one of the few to propose a framework on how PETs create business value for organizations. Future research can use factors in the framework (e.g. customer interactions, sales, personalized services and market trend prediction) to conduct a quantitative study on PETs’ business value. Managers adopting PETs can use the framework to identify areas where PETs impact their organization.

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas.

A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications.

The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research.

This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.

The rapid technological growth, changes in consumer demands, products’ built-in obsolescence, presence of more non-repairable parts, shorter lifespan, etc., lead to the generation of e-waste at an unprecedented rate. Although a number of research proposals and business products to manage e-waste exist in the literature, they lack in many aspects such as incomplete coverage of product’s life cycle, access control, payment channels (in few cases), incentive mechanisms, scalability issues, and missing experimental validation. The purpose of this paper is to introduce a novel blockchain-based e-waste management system aiming to mitigate the above-mentioned downsides and limitations of the existing proposals.

This paper proposes a robust and reliable e-waste management system by leveraging the power of blockchain technology, which captures the complete life cycle of e-products commencing from their manufacturing as new products to their disposal as e-waste and their recycling back into raw materials.

While the use of blockchain technology increases accountability, transparency and trust in the system, the proposal overcomes various challenges and limitations of the existing systems by providing seamless interactions among various agencies.

This paper presents a prototype implementation of the system as a proof-of-concept using solidity on the Ethereum platform and this paper performs experimental evaluations to demonstrate its feasibility and effective performance in terms of execution gas cost and transaction throughput.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.