A Protection Layer over MapReduce Framework for Big Data Privacy

In many organizations, big data analytics has become a trend in gathering valuable data insights. The framework MapReduce, which is generally used for this purpose, has been accepted by most organizations for its exceptional characteristics. However, because of the availability of significant processing resources, dispersed privacy-sensitive details can be collected quickly, increasing the widespread privacy concerns.  This article reviews some of the existing research articles on the MapReduce framework's privacy issues and proposes an additional layer of privacy protection over the adopted framework. The data is split into bits and processed in the clouds, and two other steps are taken. Hadoop splits the file into bits of a smaller scale. The task tracker then allocates these bits to several mappers. First, the data is split up into key-value pairs, and the intermediate data sets are generated.  The efficiency of the suggested approach may then be effectively interpreted. Overall, the proposed method provides improved scalability. The following figures compare execution time with relation to file size and the number of partitions. As privacy protection technique is used, the loss of data content can be appropriately handled.  It has been demonstrated that MRPL outperforms current methods in terms of CPU optimization, memory usage, and reduced information loss.  Research reveals that the suggested strategy creates significant advantages for Big Data by enhancing privacy and protection. MRPL can considerably solve the privacy issues in Big Data

Scalable TPTDS Data Anonymization over Cloud using MapReduce

With the rapid advancement of big data digital age, large amount data is collected, mined and published. Data publishing become day today routine activity. Cloud computing is best suitable model to support big data applications. Large number of cloud service need users to share microdata like electronic health records, data containing financial transactions so that they can analyze this data. But one of the major issues in moving toward cloud is privacy threats. Data anonymization techniques are widely used to combat with privacy concerns .Anonymizing data sets using generalization to achieve k-anonymity is one of the privacy preserving techniques. Currently, the scale of data in many cloud applications is increasing massively in accordance with the Big Data tendency, thereby making it a difficult for commonly used software tools to capture, handle, manage and process such large-scale datasets. As a result it is challenge for existing approaches for achieving anonymization for large scale data sets due to their inefficiency to support scalability. This paper presents two phase top down specialization approach to anonymize large scale datasets .This approach uses MapReduce framework on cloud, so that it will be highly scalable and efficient. Here we introduce the scheduling mechanism called Optimized Balanced Scheduling to apply the Anonymization. OBS means individual dataset have the separate sensitive field. Every data set consist of sensitive field and give priority for this sensitive field. Then apply Anonymization on this sensitive field only depending upon the scheduling. DOI: 10.17762/ijritcc2321-8169.15077

Trusted Launch of Virtual Machine Instances in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their adoption is hampered by data security concerns. At the same time, Trusted Computing (TC) is experiencing an increasing interest as a security mechanism for IaaS. In this paper we present a protocol to ensure the launch of a virtual machine (VM) instance on a trusted remote compute host. Relying on Trusted Platform Module operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for VM instances in public IaaS environments. We also present a proof-of-concept implementation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for the use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Generic Virtual Machine Images in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their faster-pased adoption is hampered by data security concerns. In the same time, Trusted Computing (TC) is experiencing a revived interest as a security mechanism for IaaS. We address the lack of an implementable mechanism to ensure the launch of a virtual machine (VM) instance on a trusted remote host. Relying on Trusted Platform Modules operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for generic VM images in public IaaS environments. We also present a proof-of-concept implemen- tation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Virtual Machine Instances in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their adoption is hampered by data security concerns. At the same time, Trusted Computing (TC) is experiencing an increasing interest as a security mechanism for IaaS. In this paper we present a protocol to ensure the launch of a virtual machine (VM) instance on a trusted remote compute host. Relying on Trusted Platform Module operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for VM instances in public IaaS environments. We also present a proof-of-concept implementation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for the use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Virtual Machine Instances in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their adoption is hampered by data security concerns. At the same time, Trusted Computing (TC) is experiencing an increasing interest as a security mechanism for IaaS. In this paper we present a protocol to ensure the launch of a virtual machine (VM) instance on a trusted remote compute host. Relying on Trusted Platform Module operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for VM instances in public IaaS environments. We also present a proof-of-concept implementation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for the use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Virtual Machine Instances in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their adoption is hampered by data security concerns. At the same time, Trusted Computing (TC) is experiencing an increasing interest as a security mechanism for IaaS. In this paper we present a protocol to ensure the launch of a virtual machine (VM) instance on a trusted remote compute host. Relying on Trusted Platform Module operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for VM instances in public IaaS environments. We also present a proof-of-concept implementation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for the use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Virtual Machine Instances in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their adoption is hampered by data security concerns. At the same time, Trusted Computing (TC) is experiencing an increasing interest as a security mechanism for IaaS. In this paper we present a protocol to ensure the launch of a virtual machine (VM) instance on a trusted remote compute host. Relying on Trusted Platform Module operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for VM instances in public IaaS environments. We also present a proof-of-concept implementation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for the use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

Trusted Launch of Generic Virtual Machine Images in Public IaaS Environments

Cloud computing and Infrastructure-as-a-Service (IaaS) are emerging and promising technologies, however their faster-pased adoption is hampered by data security concerns. In the same time, Trusted Computing (TC) is experiencing a revived interest as a security mechanism for IaaS. We address the lack of an implementable mechanism to ensure the launch of a virtual machine (VM) instance on a trusted remote host. Relying on Trusted Platform Modules operations such as binding and sealing to provide integrity guarantees for clients that require a trusted VM launch, we have designed a trusted launch protocol for generic VM images in public IaaS environments. We also present a proof-of-concept implemen- tation of the protocol based on OpenStack, an open-source IaaS platform. The results provide a basis for use of TC mechanisms within IaaS platforms and pave the way for a wider applicability of TC to IaaS security

MapReduce analysis for cloud-archived data

Public storage clouds have become a popular choice for archiving certain classes of enterprise data - for example, application and infrastructure logs. These logs contain sensitive information like IP addresses or user logins due to which regulatory and security requirements often require data to be encrypted before moved to the cloud. In order to leverage such data for any business value, analytics systems (e.g. Hadoop/MapReduce) first download data from these public clouds, decrypt it and then process it at the secure enterprise site. We propose VNCache: an efficient solution for MapReduceanalysis of such cloud-archived log data without requiring an apriori data transfer and loading into the local Hadoop cluster. VNcache dynamically integrates cloud-archived data into a virtual namespace at the enterprise Hadoop cluster. Through a seamless data streaming and prefetching model, Hadoop jobs can begin execution as soon as they are launched without requiring any apriori downloading. With VNcache's accurate pre-fetching and caching, jobs often run on a local cached copy of the data block significantly improving performance. When no longer needed, data is safely evicted from the enterprise cluster reducing the total storage footprint. Uniquely, VNcache is implemented with NO changes to the Hadoop application stack. © 2014 IEEE