Reconsidering big data security and privacy in cloud and mobile cloud systems

Large scale distributed systems in particular cloud and mobile cloud deployments provide great services improving people\u27s quality of life and organizational efficiency. In order to match the performance needs, cloud computing engages with the perils of peer-to-peer (P2P) computing and brings up the P2P cloud systems as an extension for federated cloud. Having a decentralized architecture built on independent nodes and resources without any specific central control and monitoring, these cloud deployments are able to handle resource provisioning at a very low cost. Hence, we see a vast amount of mobile applications and services that are ready to scale to billions of mobile devices painlessly. Among these, data driven applications are the most successful ones in terms of popularity or monetization. However, data rich applications expose other problems to consider including storage, big data processing and also the crucial task of protecting private or sensitive information. In this work, first, we go through the existing layered cloud architectures and present a solution addressing the big data storage. Secondly, we explore the use of P2P Cloud System (P2PCS) for big data processing and analytics. Thirdly, we propose an efficient hybrid mobile cloud computing model based on cloudlets concept and we apply this model to health care systems as a case study. Then, the model is simulated using Mobile Cloud Computing Simulator (MCCSIM). According to the experimental power and delay results, the hybrid cloud model performs up to 75% better when compared to the traditional cloud models. Lastly, we enhance our proposals by presenting and analyzing security and privacy countermeasures against possible attacks

Business intelligence meets big data : an overview on security and privacy

Today big data are the target of many research activities focusing on big data management and analysis, definition of zero latency approaches to data analytics, and protection of big data security and privacy. In particular, security and privacy are two important, while contrasting, requirements. Big data security usually refers to the use of big data to implement solutions increasing security, reliability, and safety of a distributed system. Big data privacy, instead, focuses on the protection of big data from unauthorized use and unwanted inference. In this paper, we start from the manifesto on Business Intelligence Meets Big Data [8] and the notions of full data and zero-latency analysis to discuss new challenges in the context of big data security and privacy

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Big Data Security Issues in Three Perspectives: A Review

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data.  There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30th 202

Security problems of universal data management systems

Стаття присвячена розгляду проблеми безпеки універсальних систем управління даними. Зроблено аналіз та класифікація сучасних систем управління даними за різними критеріями. На основі аналізу літератури та використання досвіду створення корпоративних систем, визначені два підходи до організації універсальних платформ управління даними: використання мультимодельних систем та інтегрованих платформ управління даними. На підставі проведеного аналізу загроз та засобів захисту даних для SQL, NoSQL, NewSQL систем управління базами даних, сховищ даних (Data WareHouse), озер даних (Data Lake) та хмар даних визначені основні підходи до захисту даних кожної категорії продуктів. Визначені сучасні тенденції розвитку технологій управління даними та засобів захисту даних. Саме стрімкий розвиток NoSQL, NewSQL систем і обмін функціональністю між ними призвів до появи систем, що мають функції багатьох класів. Визначено проблеми захисту даних для мультимодельних СУБД та інтегрованих платформ даних та запропоновано шляхи їх подолання. Адже для універсальної платформи управління даними недостатньо простої інтеграції засобів безпеки різних типів систем управління даними, необхідні нові підходи. Для інтегрованих середовищ особливої актуальності набуває підхід Data Centric Security, який орієнтовано на захист критичних даних на всіх етапах їх обробки – від збору і передачі до аналізу і розміщення в сховищах даних. Організація доступу до даних через логічні вітрини даних з використанням семантичних технологій, онтологічних моделей даних забезпечує перетворення набору розрізнених даних в єдиний масив шляхом «віртуалізації даних». Обґрунтовано актуальність та доцільність застосування когнітивних технологій та штучного інтелекту в області інформаційної безпеки, що відкрило нові можливості для створення автоматизованих, «розумних» засобів безпеки систем управління даними. Таким системам притаманна здатність до самоаналізу і конфігурування. Застосування технології машинного навчання дозволяє виявляти слабкі місця в системі безпеки СУБД. Поєднання інтелектуальних рішень безпеки та управління з технологіями баз даних дозволить швидко реагувати на нові виклики в сфері захисту сховищ та озер даних різного типуThe article deals with the security of universal data management systems. The analysis and classification of modern data management systems by different criteria has been made. Based on the analysis of the literature and the experience of creating corporate systems, two approaches to the organization of universal data management systems have been identified: the use of multimodel systems and integrated data management platforms. Based on the analysis of threats and data protection tools for database management systems SQL, NoSQL, NewSQL, Data Warehouse, Data Lake and data clouds, the main approaches to data protection of each product category have been identified. The current trends in the development of data management technologies and data security have been identified. The development of NoSQL, NewSQL systems and the exchange of functionalities between them has led to the development of systems, which have functions of many classes. The problems of data protection for multimodel database management systems and for integrated data platforms have been identified and ways to overcome the identified problems have been suggested. For a universal data management platform, it is not enough to combine security features of different types of DBMS but new approaches are needed. The Data Centric Security approach is suitable for integrated environments; it is focused on protecting critical data at all stages of their processing - from collection and transmission to analysis and deployment in data warehouses. The organization of access to data through logical data marts using semantic technologies, ontological data models provides the transformation of a set of different types of data into a single array by "data virtualization". The article has substantiated the relevance and feasibility of the use of cognitive technologies and artificial intelligence in the field of information security, which opened new opportunities for the creation of automated, "smart" security tools for data management systems. Such systems have the ability to self-analyse and configure. The use of machine learning technology allows to identify weaknesses in the database security system. The combination of intelligent security and management solutions with database technologies will allow developers to respond quickly to new challenges in the protection of integrated data management systems of various type

Development on advanced technologies – design and development of cloud computing model

Big Data has been created from virtually everything around us at all times. Every digital media interaction generates data, from computer browsing and online retail to iTunes shopping and Facebook likes. This data is captured from multiple sources, with terrifying speed, volume and variety. But in order to extract substantial value from them, one must possess the optimal processing power, the appropriate analysis tools and, of course, the corresponding skills. The range of data collected by businesses today is almost unreal. According to IBM, more than 2.5 times four million data bytes generated per year, while the amount of data generated increases at such an astonishing rate that 90 % of it has been generated in just the last two years. Big Data have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. This paper presents a view of the BD challenges and methods to help to understand the significance of using the Big Data Technologies. This article based on a bibliographic review, on texts published in scientific journals, on relevant research dealing with the big data that have exploded in recent years, as they are increasingly linked to technolog

Design and Implementation of a Distributed Encryption System for the Cloud

?Big Data ?- voluminous and variety of data from different sources. The Data can be either in the form of structure (or) unstructured Data. Privacy and security of Big Data is gaining high importance, since all the technologies are started to depend on Big Data. It is difficult to work with using most relational database management systems, desktop statistics and visualization packages since it requires massive parallel software running on tens, hundreds or even thousands of servers. In this paper, we are going to discuss the Hadoop and the method for maintaining the privacy & security of big Data. Originally Hadoop was invented without any security model. The main goal is to propose a Hadoop system that maintains privacy and security at the client system. Advanced Encryption Standard (AES) enables protection to data at each cluster, it performs encryption/decryption before read/write respectively and we are using SHA 1 for user authorization