Secure Data Sharing and Processing in Heterogeneous Clouds

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users'application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

This paper explores secure data processing in hybrid clouds wherein local computing capability is exploited alongside public cloud services to deliver an efficient and secure data management solution. Hybrid clouds offer numerous advantages including the ability to selectively outsource data and computations based on sensitivity/confidentiality. Data processing in hybrid clouds must address two interrelated challenges: (i) data distribution: how is data distributed across public and private machines, and (ii) distributed query processing: how are queries executed efficiently without leaking sensitive data to untrusted public machines. This paper addresses these challenges and incorporates the respective solutions into an add-on tool for a Hadoop, Spark, and Hive based cloud computing infrastructure. Our results show performance advantages in using our strategy as compared to other secure alternatives, even when the percentage of sensitive data is as high as 50%.

To avoid making data equivalent to big price it is significant to investigate how to deploy the VMs so as to lower both communication cost and deployment cost for streaming data processing (SBDP). The authors motivate such a bicriteria problem by considering task scheduling and resource allocation for SBDP in geo-distributed datacenters. They introduce some representative data processing frameworks in Clouds, with a special emphasize on SBDP. To support efficient data processing and analysis, service providers need to characterize the features of the different data services and propose new processing models. The geo-distributed datacenters are running rich and complex tasks to support a variety of user requirements. To improve the service performance and guarantee the quality of service at the same time, task scheduling and resource allocation algorithm in a Cloud platform is a fundamental issue in achieving efficient Cloud services.

As cloud computing brings ease of use and cost-saving features, the security and privacy of data is simultaneously becoming very challenging. The security of cloud data can be classified into two aspects: static storage security and dynamic processing security. Current researches only focus on either storage security or protecting the safety of data processing service. To offer a comprehensive data security solution, we present a secure data storage and processing framework (SDSPF) for cloud computing systems, which protects the whole life cycle of user data.KeywordsCloud computingdata securitystorage systemattribute-based encryptiontrusted execution environmentmemory protection

We propose a trust framework model for an educational cloud which is designed to overcome inequalities of M-shaped incomes by simplifying contract-based sharing of resources among multiple schools. We use class algebra, which is similar to SROIQ(D) description logic, as the access control policy language. Benefiting from the high expressivity of class algebra, our model can use implicit relations and reasoning to enforce access control that corresponds to the contracts. The security mechanism allows one-time user login to obtain all of this userâs credentials for accessing services. In addition, our model can select appropriate material to adapt to the level of the student by adding the classification information to the attributes of the educational material and the student, and integrating this information into the access control policy to build an exclusive learning graph for each student.

Modernization in various sectors of life has made data and information valuable, which has an impact in changing people’s lives. One of the changes is how people get information from a large amount of data to be used in the public development sector. Public policy, as a solution to solve various public problems, needs to be supported by accurate data and information facts. With a massive amount of data, this has an impact on public sector organizations to be able to store and analyze various types of information for public policies consideration. The term ‘Society 5.0’ has emerged, a concept that refers to revolution in people’s lives through the use of technology by considering the humanities aspect. The potential for the huge benefits of big data technology is interesting to know as to whether big data can be used for the public policy formulation process. Therefore, this article will provide an overview of the implications of big data for the public policy process in society. This research uses qualitative method with literature study approach. The data came from websites and government documents related to the use of big data in public policies, analyzed using the NVivo12 Plus application. The emergence of big data, as a basis for making public policy, can be utilized for analysis, which includes social data analysis, historical data analysis, and predictive data analysis which can influence accuracy in the policy decision making. Public policy in the era of society 5.0 is vital to be realized through the use of big data as a source of information supported by technology and the readiness of actors in the policy-making process.

In this study, blockchain technology is used to strengthen the security of wireless sensing data in the network architecture of the Internet of Things. For many organic farms that contain crops with high unit prices, the environmental parameters of the crops grown on the farm are often regarded as confidential data. First, a wireless sensing network is used to transmit sensing data to the farm's front-end integrated microcontroller for fusion of the sensing data. After fusion of the sensing data is completed, the processed data is transmitted to the data processing center of the entire farm for encapsulation of the blockchain algorithm. Data are sent to the cloud database for storage after they are packaged by the system. At the same time, the data in the cloud database can be managed and analyzed by remote operators. Data encapsulation based on blockchain technology is used to effectively prevent any data from being stolen or destroyed by hackers. A complete blockchain encapsulation database system is implemented in the experimental stage, and the operator can use this system to encrypt the data that have been fused at the remote end for blockchain technology. In the encryption process of this blockchain, the security of data processing can be specifically enhanced by the system. Finally, each encapsulated block of data is securely stored in a private cloud database. There is a function to check whether the data in the cloud have been tampered with. A very innovative approach to using blockchain technology is proposed in this article to enhance the security of data processing.

In this article we were studying the types of architectures for cloud processing and storage of data, data consolidation and enterprise storage. Special attention is given to the use of large data sets in computational process. It was shown, that based on the methods of theoretical analysis and experimental study of computer systems architectures, including heterogeneous, special techniques of data processing, large volumes of information models relevant architectures, methods of optimization software for the heterogeneous systems, it is possible to ensure the integration of computer systems to provide computations with very large data sets.

With the tremendous development of remote sensing technologies, large-scale data sets are collected from environmental and meteorological sensors. Edge computing for large-scale data sets is hard to be conducted in real time on the sensors, thus data transfer to cloud system and data processing play an important role. One of the barriers in such system is high-speed data transfer on long-distance networks. Even low packet loss condition leads to decrease in throughput by conventional transmission control protocol (TCP)-based tools. In this paper, we overcome this issue by proposing a high-performance data transfer tool to improve real-time data processing of phased array weather radar on long-distance networks. The proposed tool, called high-performance copy (hcp), is designed based on an open source tool for file copy operations and implemented using our socket library specialized for long-distance networks. We evaluate our tool with simulations and real data transfer scenarios. The results show that the hcp enables concurrent data transfer from radar sites to cloud system (data processing site) in real time. The throughput of data transfer from a radar site to a data processing site increases from 40 Mbps using the conventional TCP-based tool to 489.2 Mbps using the hcp.

Data transfer is an indispensable step that is widely involved in the maintenance and processing of Cloud data. Due to rapid growth in Cloud data, methods of reducing the huge energy consumption of data transfer in the Cloud have become a challenge. In this paper, we propose a novel energy-efficient data transfer strategy called link rate controlled data transfer LRCDT. By scheduling bandwidth in a link rate controlled fashion, LRCDT intends to reduce the energy consumption specifically for data transfer that does not require the maximum transfer speed, which is referred to as 'lazy' data transfer, so to achieve the energy-efficient data transfer goal in the overall term. The result in our simulation indicates that LRCDT is able to reduce energy consumption by up to 63% when compared to existing data transfer strategies.

Homomorphic cryptosystem-based secure data processing model for edge-assisted IoT healthcare systems

Quantum computing was in its infancy while cloud adoption increased but secure data processing methods in distributed environments became more important. As cloud-based operations continue to expand, allowing computation to be performed directly on encrypted data without the need for exposing private keys will be an important use case of homomorphic encryption. Fully homomorphic encryption (FHE) encompasses both addition and multiplication, whereas partial homomorphic encryption (PHE) is limited to either type of operation, which can provide practical efficiency benefits for certain applications. In this research, LightPHE, a Python-based PHE framework is implemented along with the implementation performance and environmental sustainability evaluation. The evaluation scope extends beyond the typical fair assessment of cryptography to include energy consumption profile and carbon emissions. The framework combines proven PHE algorithms and stays true to modular design principles as the foundation for secure application development. Experimental evaluations were performed on several cloud platforms such as Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark. The performance evaluation has included key generations, encryption, decryption and homomorphic operation, also the energy consumption was based on computational resource utilization. The environmental impact was evaluated via thorough analysis of Scope 1-3 emissions, as well as standardized data center efficiency metrics based on regional carbon intensity data. The study’s results showed unique trends in the trade-offs between computational performance and energy efficiency. Rather than calculating emissions for every algorithm variant, the analysis focuses on low and mid impact cases such as 80-bit and 128-bit resource-intensive homomorphic operations—where environmental considerations are most critical with Colab L4 GPU. Since the comparison with all cpu and gpu types including all data centers that will lead to another research. The results suggested that in both high-performance configurations and distributed environments, different optimization characteristics emerged in the energy-energy view where a lower total energy consumption is observed even at the expense of higher instantaneous demand. LightPHE offered uniform security across configurations, but differing environmental impact. This work empirically demonstrates the environmental considerations inherent to cryptographic implementations and lays the groundwork for future work in quantifiably assessing both security and sustainability in cloud-based encryption systems. The findings of this research provide practical recommendations for organizations looking to deploy secure, efficient, and sustainable data processing systems in modern cloud settings.

Purpose. The aim of the article is to analyse the processing of personal data of air passengers during the SARS-CoV-2 pandemic in the context of doubts that have arisen in connection with the need for these passengers to provide their personal data as part of filling out the Passenger Location Card questionnaire. Method. The research method used in this study is case study. Findings. In the study, it was showed that firstly, the data of air passengers processed in relation to the application of the Passenger Location Card by the State Border Sanitary Inspectorate in Warsaw should be protected under the provisions of the General Regulation on the protection of personal data. Furthermore, their controller, i.e. the State Border Sanitary Inspectorate in Warsaw, did not fulfil its obligations in this regard. This, in effect, justifies the conclusion that the processing process not in accordance with the law on the protection of personal data. Research and conclusions limitations. The analysis concerned only passengers of aircrafts arriving and/or departing from airports located on the territory of the Republic of Poland. Practical implications. The analysis carried out in this study may provide a solution to the issues that have arisen in the public sector with regard to the processing of personal data collected from air passengers on the basis of the Passenger Location Card questionnaire and thus, the conclusions may prove useful for data controllers who should be aware of such problems, but also for air travellers as data subjects who should be protected by the General Data Protection Regulation and their rights in this regard. Originality. This analysis, if only for the reason that it is an analysis of a problem that has come to light relatively recently (March 2020), has so far, only been the subject of consideration in press articles.

The EU data protection reform package adopted in 2016 introduced two main legal instruments regulating the processing of personal data, namely the General Data Protection Regulation (GDPR) applicable to all data processing operations carried out in both private and public sector with a few explicitly enumerated exceptions, and the Law Enforcement Directive (LED) limited to the processing of personal data "by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties". Since these two legal acts constitute separate legal regimes with different thresholds for the protection of personal data, it is crucial to draw precise borderlines between the scopes of their application. With the assumption that where the specific rules of the LED are not applicable, the general rules of the GDPR should apply, this chapter focuses on the former instrument and aims at providing a comprehensive analysis of the material scope of its application. In doing so, it identifies some areas of uncertainty, which if not hamper, then at least, bring into question the process of harmonization of the rules for data processing in the law enforcement context.

Financial Intelligence Units (FIUs) are key players in the current Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) legal system. FIUs are specialised bodies positioned between private financial institutions and states’ law enforcement authorities, what renders them a crucial middle link in the chain of information exchange between the private and public sectors. Considering that a large share of this information is personal data, its processing must meet the minimum data protection standards. Yet, the EU data protection legal framework is composed of two main instruments, i.e. the General Data Protection Regulation (GDPR) and the Law Enforcement Directive (LED), which provide different thresholds for the protection of personal data. The aim of this paper is to clarify the applicable data protection legal regime for the processing of personal data by FIUs for AML/CFT purposes. To that end, the paper provides an overview of the nature and goals of AML/CFT policy and discusses the problem of the diversity of existing FIU models. Further, it proposes a number of arguments in favour of and against the possibility of applying either the GDPR or LED to the processing of personal data by the FIUs and reflects on how convincingly these arguments can be used depending on the specificities of a given FIU model.