Final year IEEE Bigdata projects hadoop 2018-2019

For Outstation Students, we are having online project classes both technical and coding using net-meeting software

For details, Call: 9886692401/9845166723

DHS Informatics providing latest 2018-2019 Final year IEEE Bigdata projects hadoop 2018-2019  on BigData/ Hadoop for the final year engineering students. First trains all students to develop their IEEE Bigdata projects hadoop with good idea what they need to submit in college to get good marks.We are providing IEEE bigdata projects hadoop  for B.E / B.TECH, M.TECH, MCA, BCA, DIPLOMA students from more than two decades.

BIGDATA

Abstract: The mission of subspace clustering is to find hidden clusters exist in different subspaces within a dataset. In recent years, with the exponential growth of data size and data dimensions, traditional subspace clustering algorithms become inefficient as well as ineffective while extracting knowledge in the big data environment, resulting in an emergent need to design efficient parallel distributed subspace clustering algorithms to handle large multi-dimensional data with an acceptable computational cost. In this paper, we introduce MR-Mafia: a parallel mafia subspace clustering algorithm based on MapReduce. The algorithm takes advantage of MapReduce’s data partitioning and task parallelism and achieves a good tradeoff between the cost for disk accesses and communication cost. The experimental results show near linear speedups and demonstrate the high scalability and great application prospects of the proposed algorithm.                                                                                                                                                                                                                                                                                                                                         Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract : Personal Health Record (PHR) is a patient-centric model of health information exchange, which greatly facilitates the storage, access and share of personal health information. In order to share the valuable resources and reduce the operational cost, the PHR service providers would like to store the PHR applications and health information data in the cloud. The private health information may be exposed to unauthorized organizations or individuals since the patient lost the physical control of their health information. Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) is a promising solution to design cloud-assisted PHR secure sharing system. It provides fine-grained access control, confidentiality, authenticity and sender privacy of PHR data.  In order to reconcile the conflict of high computational overhead and low efficiency in the designcryption process, an outsourcing scheme is proposed in this paper. In our scheme, the heavy computations are outsourced to Ciphertext Transformed Server (CTS), only leaving a small computational overhead for the PHR user. At the same time, the extra communication overhead in our scheme is actually tolerable.                                                                                                                                                                       Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract : The skyline operator has attracted considerable attention recently due to its broad applications. However, computing a skyline is challenging today since we have to deal with big data. For data intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose the efficient parallel algorithm SKY-MR+ for processing skyline queries using MapReduce. We first build a quadtree-based histogram for space partitioning by deciding whether to split each leaf node judiciously based on the benefit of splitting in terms of the estimated execution time. In addition, we apply the dominance power filtering method to effectively prune non-skyline points in advance. We next partition data based on the regions divided by the quadtree and compute candidate skyline points for each partition using MapReduce. Finally, we check whether each skyline candidate point is actually a skyline point in every partition using MapReduce.                                                                                                                                                                                                                                                      Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract: Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services. However, secure data sharing is problematic. This paper proposes a framework for secure sensitive data sharing on a big data platform, including secure data delivery, storage, usage, and destruction on a semi-trusted big data sharing platform. We present a proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protectionmethod based on a virtual machinemonitor, which provides support for the realization of system functions. The framework protects the security of users’ sensitive data effectively and shares these data safely. At the same time, data owners retain complete control of their own data in a sound environment for modern Internet information security.      Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract :Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud.                                                                                                                                                                                                                                                                                                                                            Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Final year IEEE Bigdata projects hadoop 2018-2019

Project CODE
TITLES
BASEPAPER
SYNOPSIS
LINKS
1. IEEE 2018: MR-Mafia: Parallel Subspace Clustering Algorithm Based on MapReduce For Large Multi-dimensional Datasets Title Title Title
2. IEEE 2018: Ciphertext-Policy Attribute-Based Signcryption With Verifiable Outsourced Designcryption for Sharing Personal Health Records Title Title Title
3. IEEE 2018: Client Side Secure Image Deduplication Using DICE Protocol Title Title Title
4. IEEE 2018: Secure Identity-based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing Title Title Title
5. IEEE 2018: Capacity-aware Key Partitioning Scheme for Heterogeneous Big Data Analytic Engines Title Title Title
6. IEEE 2018: Privacy preserving Reverse k-Nearest Neighbor Queries Title Title Title
7. IEEE 2017: Efficient Processing of Skyline Queries Using MapReduce Title Title Title
8. IEEE 2017: FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters Title Title Title
9. IEEE 2017: Secure Sensitive Data Sharing on a Big Data Platform Title Title Title
10. IEEE 2017: Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset Title Title Title
11. IEEE 2017:  SocialQ&A: An Online Social Network Based Question and Answer System Title Title Title
12. IEEE 2017:  A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds Title Title Title
13. IEEE 2017: Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport Title Title Title
14. IEEE 2017: Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing Title Title Title
15. IEEE 2017: Attribute-Based Storage Supporting Secure Duplication of Encrypted Data in Cloud Title Title Title
16. IEEE 2017:  Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network Title Title Title
17. IEEE 2017:  Cost-Aware Big Data Processing across Geo-distributed Datacenters Title Title Title
18. IEEE 2017:  Towards Secure Data Sharing in Cloud Computing Using Attribute Based Proxy  Re-Encryption with Keyword Search Title Title Title
19. IEEE 2017: Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series Title Title Title
20. IEEE 2017:  Spamdoop: A privacy-preserving Big Data platform for collaborative spam detection Title Title Title

Students’ stratification only important, we first brief the students about the technologies and type of Final year IEEE Bigdata projects hadoop 2018-2019 and other domain projects. After complete concept explanation of the Final year IEEE Bigdata projects hadoop 2018-2019, students are allowed to choose more than one Final year IEEE Bigdata projects hadoop 2018-2019 for functionality details. Even students can pick one project topic from Final year IEEE Bigdata projects hadoop 2018-2019 and another two from other domains like data mining, image process, information forensic etc.

Final year IEEE Bigdata projects hadoop 2018-2019

Big Data is having a massive growth in application industry as well as in growth of Real time applications and technologies, Big Data can be used with automatic and semiautomatic in a lot of ways such as for huge data with the Encryption and decryption Techniques as well as executing the commands. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.

IEEE bigdata projects hadoop

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IEEE bigdata projects hadoop

dhs Javaprojects Bigdata

IEEE bigdata projects hadoop

dhs Javaprojects Bigdata

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Final year IEEE Bigdata projects hadoop 2018-2019
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Final year IEEE Bigdata projects hadoop 2018-2019
Provider Name
DHS Informatics,
Bangalore,Jayanagar,South India-560011,
Telephone No.9886692401
Area
CSE, ECE, ISE, Mechanical Engineering
Description
DHS is guiding PHD, M.Tech, BE, MCA, BCA, Diploma students in their academic projects and supported the students in various innovations. For final year students DHS Informatics provide project training in domain knowledge and technical knowledge and guided them in their final year & mini projects.