Secure & Encrypted Accessing and Sharing of Data in Distributed Virtual Cloud: A Review

Cloud Computing has been accepted as the next generation architecture of IT Enterprise. The Cloud computing idea offers dynamically scalable resources provisioned as a service over and the Internet Economic benefits are the main driver for the Cloud, since it promises the reduction of capital expenditure and operational expenditure Placing critical data in the hands of a cloud provider should come with the guarantee of security and availability for data and in use. various alternatives available for storage services, while data confidentiality is the solutions for the database as a service pattern are still undeveloped This architecture is supporting purely distributed clients to connect directly to an encrypted cloud database, and to execute simultaneous and independent operations including those modifying the database structure. The Access control policy is set out in which only authorised users are able to decrypt the stored information. This scheme prevents from replay attacks and supports formation, modification, and reading data stored in the cloud. This unique attribute, however, creates many new security challenges which have not been well understood. Security is to protect data from danger and vulnerability. There are various dangers and vulnerabilities to be handle. Various security issues and some of their solution are explained and are concentrating mainly on public cloud security issues and their solutions. Data should always be encrypted in a time when stored and transmitted

Assessing the vulnerabilities and securing MongoDB and Cassandra databases

Due to the increasing amounts and the different kinds of data that need to be stored in the databases, companies, and organizations are rapidly adopting NoSQL databases to compete. These databases were not designed with security as a priority. NoSQL open-source software was primarily developed to handle unstructured data for the purpose of business intelligence and decision support. Over the years, security features have been added to these databases but they are not as robust as they should be, and there is a scope for improvement as the sophistication of the hackers has been increasing. Moreover, the schema-less design of these databases makes it more difficult to implement traditional RDBMS like security features in these databases. Two popular NoSQL databases are MongoDB and Apache Cassandra. Although there is a lot of research related to security vulnerabilities and suggestions to improve the security of NoSQL databases, this research focusses specifically on MongoDB and Cassandra databases. This study aims to identify and analyze all the security vulnerabilities that MongoDB and Cassandra databases have that are specific to them and come up with a step-by-step guide that can help organizations to secure their data stored in these databases. This is very important because the design and vulnerabilities of each NoSQL database are different from one another and hence require security recommendations that are specific to them

Data Mining with Big data applications, its challenges and Future Research

Big data is the term for a collection of data sets which are enormous and complex, it contain organized and unstructured both kind of data. Data originates from all over the place, sensors used to assemble atmosphere information, presents via web-based networking media destinations, computerized pictures and recordings and so forth, This data is known as big data. Valuable data can be separated from this big data with the assistance of data mining. Data mining is a strategy for finding intriguing examples just as enlightening, reasonable models from enormous scale data. Right now reviewed sorts of big data and difficulties in big data for future. Separating valuable information from huge data-set like in all science and designing space, There will be most energizing open door in up and coming a very long time for big data. This paper incorporates big data, Data mining, Data mining with big data, Challenging issue and study papers of different organizations identified with big-data. Each organization concentrated on the most proficient method to oversee huge arrangement of data and how much organizations put resources into big-data just as what kind of return they get. Numerous specialized difficulties like implementations and visualizations are to be thought about in future. To oversee and dissect edge data investigate business openings getting from the research of edge data. Team up with the business to comprehend existing edge framework and the potential use for data. It concluded from the discoveries that Enterprise are as yet searching for the correct foundation instruments that will empower them to successfully deal with their big-data with their business needs

Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data

Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works

Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data