A Security and Privacy-Preserving Path for Enhancing Information Systems that Manage Cross-Cloud Applications

2020 ◽  
pp. 1119-1132
Author(s):  
Yiannis Verginadis ◽  
Ioannis Patiniotakis ◽  
Marcin Prusinski ◽  
Marta Rozanska ◽  
Sebastian Schork ◽  
...  
Keyword(s):  
Information Systems ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Cloud Applications

scholarly journals Understanding and Enhancing Consumer Privacy Perceptions in the Cloud

2020 ◽  
pp. 59-78
Author(s):  
Grace Fox
Keyword(s):  
Cloud Computing ◽  
Information Systems ◽  
Security And Privacy ◽  
Complex Domain ◽  
Consumer Privacy ◽  
Recent Increase ◽  
Technical Solutions ◽  
And Control ◽  
Cloud Applications

Abstract The recent increase in highly publicised cloud breaches, coupled with issues surrounding transparency and control in the cloud, highlights the importance of understanding and addressing privacy in this context. The extant cloud privacy literature has a tendency to focus on technical solutions to address security and privacy together, but a small emerging body of literature acknowledges the importance of consumers’ privacy perceptions in the context of cloud computing. Given the breadth of cloud applications and the situational nature of privacy, it is imperative to unpack the role of privacy in this complex domain. This chapter leverages the broader privacy literature in the Information Systems field to identify potential measures to enhance consumer privacy in the cloud context and highlights a number of paths for research to further our knowledge of consumer privacy perceptions in the various cloud contexts.

scholarly journals Blockchain, Enterprise Resource Planning (ERP) and Accounting Information Systems (AIS): Research on e-Procurement and System Integration

2021 ◽  
Vol 11 (15) ◽  
pp. 6792
Author(s):  
Alessio Faccia ◽  
Pythagoras Petratos
Keyword(s):  
Information Systems ◽  
Enterprise Resource Planning ◽  
System Integration ◽  
Case Study Research ◽  
Accounting Information ◽  
Resource Planning ◽  
Security And Privacy ◽  
Accounting Information Systems ◽  
Erp System

Accounting information systems (AISs), the core module of any enterprise resource planning (ERP) system, are usually designed as centralised systems. Nowadays, the continuous development and applications of blockchain, or more broadly—distributed ledger technology (DLT), can change the architecture, overcome and improve some limitations of centralised systems, most notably security and privacy. An increasing number of authors are suggesting the application of blockchain technologies in management, accounting and ERPs. This paper aims to examine the emerging literature on this field, and an immediate result is that blockchain applications can have significant benefits. The paper’s innovative contribution and considerable objective are to examine if blockchain can be successfully integrated with AIS and ERPs. We find that blockchain can facilitate integration at multiple levels and better serve various purposes as auditing compliance. To demonstrate that, we analyse e-procurement systems and operations using case study research methodology. The findings suggest that DLT, decentralised finance (DeFI), and financial technology (FinTech) applications can facilitate integrating AISs and ERP systems and yield significant benefits for efficiency, productivity and security.

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-22
Author(s):  
Qiang Yang
Keyword(s):  
Private Information ◽  
High Performance ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Brute Force ◽  
New Paradigm ◽  
User Privacy ◽  
Data Governance ◽  
Incentive Mechanisms ◽  
Rule Out

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

Cloud Computing for Cytopathologists

2014 ◽  
pp. 250-271 ◽  
Author(s):  
Abraham Pouliakis ◽  
Stavros Archondakis ◽  
Efrossyni Karakitsou ◽  
Petros Karakitsos
Keyword(s):  
Cloud Computing ◽  
Computer Software ◽  
Security And Privacy ◽  
Software Systems ◽  
System Architectures ◽  
Use Of Resources ◽  
Processing Power ◽  
Software And Hardware ◽  
Cloud Applications ◽  
Near Future

Cloud computing is changing the way enterprises, institutions, and people understand, perceive, and use current software systems. Cloud computing is an innovative concept of creating a computer grid using the Internet facilities aiming at the shared use of resources such as computer software and hardware. Cloud-based system architectures provide many advantages in terms of scalability, maintainability, and massive data processing. By means of cloud computing technology, cytopathologists can efficiently manage imaging units by using the latest software and hardware available without having to pay for it at non-affordable prices. Cloud computing systems used by cytopathology departments can function on public, private, hybrid, or community models. Using cloud applications, infrastructure, storage services, and processing power, cytopathology laboratories can avoid huge spending on maintenance of costly applications and on image storage and sharing. Cloud computing allows imaging flexibility and may be used for creating a virtual mobile office. Security and privacy issues have to be addressed in order to ensure Cloud computing wide implementation in the near future. Nowadays, cloud computing is not widely used for the various tasks related to cytopathology; however, there are numerous fields for which it can be applied. The envisioned advantages for the everyday practice in laboratories' workflow and eventually for the patients are significant. This is explored in this chapter.

Security and Privacy Issues in IoT

2019 ◽  
pp. 129-156
Author(s):  
S. R. Mani Sekhar ◽  
Sharmitha S. Bysani ◽  
Vasireddy Prabha Kiranmai
Keyword(s):  
Cloud Computing ◽  
Information Systems ◽  
Internet Of Things ◽  
Case Studies ◽  
Communication Systems ◽  
Fog Computing ◽  
Security And Privacy ◽  
Privacy Issues

Security and privacy issues are the challenging areas in the field of internet of things (IoT) and fog computing. IoT and fog has become an involving technology allowing major changes in the field of information systems and communication systems. This chapter provides the introduction of IoT and fog technology with a brief explanation of how fog is overcoming the challenges of cloud computing. Thereafter, the authors discuss the different security and privacy issues and its related solutions. Furthermore, they present six different case studies which will help the reader to understand the platform of IoT in fog.

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

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