FINANCIAL DATA SECURITY IN CLOUD COMPUTING

Many enterprises have achieved a novel, quick, and low-costtechnology to address the concerns of data storage and availabilityfor customers, which is referred to as Cloud Computing. More andmore financial service companies and organizations have shifted theiroffline services to cloud platforms to provide customers with moreconvenient and accurate services. Working with a cloud servicesprovider offers a wide variety of benefits for banks and financialinstitutions. This includes greater flexibility and scalability, lowercosts, and improved organizational efficiency. However, at themoment, they pose a certain level of data security risk to financialorganizations. For financial institutions, keeping data secure is of theutmost importance. Financial information is extremely sensitive,making it valuable and especially vulnerable. The cloud serviceproviders are making significant efforts to develop the cloud industryin order to maintain optimal security. After discussing cloudcomputing in the financial sector, this research outlined six majorsecurity concerns that financial institutions face in cloud computing.They are Information security Business securitySystem Security HostSecurity Data Security Network Security. We also discussed themajor strategies by cloud computing providers to tackle theseissues.Once the security challenges can be resolved properly, cloudcomputing will be further promoted in the finance sector. Financialinstitutions may leverage cloud computing in the future to developnovel business models and provide customers with an entirely newexperience with financial services.

A Study Internet of Things Is A Revolutionary Approach for Future Technology Enhancement

The creation is pushing ahead on a quick leap, and the recognition goes to regularly developing innovation. One such idea is Internet of things with which robotization is never again an augmented simulation. IOT interfaces different nonliving articles through the web and empowers them to impart data to their locale system to computerize forms for people and makes their lives simpler. The paper shows what's to come difficulties of IoT ,, for example, the specialized (network , similarity and life span , guidelines , insightful investigation and activities , security), business ( venture , unassuming income model and so forth ), cultural (evolving requests , new gadgets, cost, client certainty and so forth ) and lawful difficulties ( laws, guidelines, methodology, approaches and so on ). An area additionally examines the different fantasies that may hamper the advancement of Internet of things, security of information being the most basic factor of all. An idealistic way to deal with individuals in embracing the unfurling changes brought by IOT will likewise benefit in its development. Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

Key Factors in the Implementation of the Internet of Things in the Hotel Sector

Many factors can influence decision-making, and if you wish to know which are the most influential factors in a decision, they must be classified by their degrees of influence. This study seeks to determine the most influential factors in the decision of hotels to accept and implement the Internet of Things in their services through a literary review and a causal study carried out on experts in technology and hotels. The methodology involves the use of fuzzy cognitive maps and the FCMappers tool. The results obtained show that the following factors are among the most influential (in order of relevance): the perceived reliability of the technology, the relative advantage it gives, the level of top management support, compatibility, customer pressure, information systems provider support, security, business partner pressure, characteristics of the leader or manager, government pressure or incentives, pressure from competitors, technological organizational readiness, complexity, size of the company, and the perceived cost.

Trends in Using IoT with Machine Learning in Health Prediction System

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

How Microsoft Teams is Evolving

When the pandemic caused chaos across our working lives, Microsoft Teams, along with other collaboration tools, helped to keep the UK working. Nick Hedderman, Director, Modern Work & Security Business Group, Microsoft UK, spoke to Johanna Hamilton AMBCS about this new normal.

An Adaptively Secure Functional Encryption for Randomized Functions

Functional encryption (FE) can provide a fine-grained access control on the encrypted message. Therefore, it has been applied widely in security business. The previous works about functional encryptions most focused on the deterministic functions. The randomized algorithm has wide application, such as securely encryption algorithms against chosen ciphertext attack, privacy-aware auditing. Based on this, FE for randomized functions was proposed. The existing constructions are provided in a weaker selective security model, where the adversary is forced to output the challenge message before the start of experiment. This security is not enough in some scenes. In this work, we present a novel construction for FE, which supports the randomized functionalities. We use the technology of key encapsulated mechanism to achieve adaptive security under the simulated environment, where the adversary is allowed to adaptively choose the challenge message at any point in time. Our construction is built based on indistinguishability obfuscation, non-interactive witness indistinguishable proofs and perfectly binding commitment scheme.

Strategies to balance energy security, business, trade and sustainable development: selected case studies†

Energy is pivotal for socio-economic and cultural development. Last century witnessed a drastic increase, on one hand on the consumption of energy and, on the other on greenhouse gases emissions. Traditionally, energy security has been linked with the need to guarantee supply and, in turn, enables economic growth. Against this background, countries focused on diversifying both energy sources and trade partners while at the same time increasing investment in energy infrastructure and technology. Investment in low-carbon energy sources for enhancing national energy policies prompts for a new understanding of energy security. The aim is, in fact, not anymore limited to securing provision but also to strengthen diversification and counteract the negative effects of energy consumption on the environment. The need to include a sustainability component to energy in trade, business and in the society at large, is adding a further layer of complexity in shaping national and international energy policy. Strategies to balance energy security, business, trade, and sustainable development are urgently needed in the Anthropocene. Creative and innovative approaches to energy policy could be found in countries where energy consumption is on a steady rise and environmental degradation is crystal clear.