Final year IEEE Bigdata projects hadoop 2019-2020

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 2019-2020 Final year IEEE Bigdata projects hadoop 2019-2020  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 deduplication based on attribute-based encryption can be well used in eHealth systems to save storage space and share medical records. However, the excessive computation costs of existing schemes lead to inefficient deduplication. In addition, the frequent changes of clients’ attribute weaken the forward secrecy of data, and thus, how to achieve the attribute revocation in deduplication is a problem that remains to be solved. In this paper, we propose a variant of the attribute-based encryption scheme that supports efficient deduplication and attributes revocation for eHealth systems. Specifically, an efficient deduplication protocol based on the nature of prime number is used to alleviate the computation burden on the private cloud, and attribute revocation is realized by updating the attribute agent key and the ciphertext. Moreover, outsourcing decryption is introduced to reduce the computation overhead of clients. The security analysis argues that the proposed scheme can reach the desired security requirements, and the visual experiment result indicates the excellent performance of the proposed scheme if while realizing deduplication and attribute revocation.

   Contact: 

   +91-98451 66723

  +91-98866 92401

Abstract:Secure data deduplication can significantly reduce the communication and storage overheads in cloud storage services, and has potential applications in our big data-driven society. Existing data deduplication schemes are generally designed to either resist brute-force attacks or ensure the efficiency and data availability, but not both conditions. We are also not aware of any existing scheme that achieves accountability, in the sense of reducing duplicate information disclosure (e.g., to determine whether plaintexts of two encrypted messages are identical). In this paper, we investigate a three-tier cross-domain architecture, and propose an efficient and privacy-preserving big data deduplication in cloud storage (hereafter referred to as EPCDD). EPCDD achieves both privacy-preserving and data availability, and resists brute-force attacks. In addition, we take accountability into consideration to offer better privacy assurances than existing schemes. We then demonstrate that EPCDD outperforms existing competing schemes, in terms of computation, communication and storage overheads. In addition, the time complexity of duplicate search in EPCDD is logarithmic.

 

   Contact: 

   +91-98451 66723

  +91-98866 92401

ABSTRACT

In this paper, we propose a novel secure role re-encryption system (SRRS), which is based on convergent encryption and the role re-encryption algorithm to prevent the privacy data leakage in cloud and it also achieves the authorized deduplication and satises the dynamic privilege updating and revoking. Meanwhile, our system supports ownership checking and achieves the proof of ownership for the authorized users efficiently. Specifically, we introduce a management center to handle with the authorized request and establish a role authorized tree (RAT) mapping the relationship of the roles and keys. With the convergent encryption algorithm and the role re-encryption technique, it can be guaranteed that only the authorized user who has the corresponding role re-encryption key can access the specic le without any data leakage. Through role re-encryption key updating and revoking, our system achieves the dynamic updating of the authorized user’s privilege. Furthermore, we exploit the dynamic count lters (DCF) to implement the data updating and improve the retrieval of ownership verifying effectively. We conduct the security analysis and the simulation experiment to demonstrate the security and efficiency of our proposed system.

 

   Contact: 

   +91-98451 66723

  +91-98866 92401

ABSTRACT

 

With the rapid development of Internet technology and social networks, a large number of comment texts are generated on the Web. In the era of big data, mining the emotional tendency of comments through artificial intelligence technology is helpful for the timely understanding of network public opinion. The technology of sentiment analysis is a part of artificial intelligence, and its research is very meaningful for obtaining the sentiment trend of the comments. The essence of sentiment analysis is the text classification task, and different words have different contributions to classification. In the current sentiment analysis studies, distributed word representation is mostly used. However, distributed word representation only considers the semantic information of word, but ignore the sentiment information of the word. In this paper, an improved word representation method is proposed, which integrates the contribution of sentiment information into the traditional TF-IDF algorithm and generates weighted word vectors. The weighted word vectors are input into bidirectional long short term memory (BiLSTM) to capture the context information effectively, and the comment vectors are better represented. The sentiment tendency of the comment is obtained by feed forward neural network classifier. Under the same conditions, the proposed sentiment analysis method is compared with the sentiment analysis methods of RNN, CNN, LSTM, and NB. The experimental results show that the proposed sentiment analysis method has higher precision, recall, and F1 score. The method is proved to be effective with high accuracy on comments.

 

   Contact: 

   +91-98451 66723

  +91-98866 92401

Abstract

As one important technique of fuzzy clustering in data mining and pattern recognition, the possibility c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogeneous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on Map Reduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.

 

   Contact: 

   +91-98451 66723

  +91-98866 92401

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

  +91-98866 92401

                                                                                                                                                                                                                                                                                                                           

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

  +91-98866 92401

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

  +91-98866 92401

                                                                                                                                                                                                                          

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

  +91-98866 92401

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

  +91-98866 92401

                                                                                                                                                                                                                                                                                                                     

Final year IEEE Bigdata projects hadoop 2019-2020

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 2019-2020 and other domain projects. After complete concept explanation of the Final year IEEE Bigdata projects hadoop 2019-2020, students are allowed to choose more than one Final year IEEE Bigdata projects hadoop 2019-2020 for functionality details. Even students can pick one project topic from Final year IEEE Bigdata projects hadoop 2019-2020 and another two from other domains like data mining, image process, information forensic etc.

Final year IEEE Bigdata projects hadoop 2019-2020

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

dhs Javaprojects Bigdata

IEEE bigdata projects hadoop

dhs Javaprojects Bigdata

IEEE bigdata projects hadoop

dhs Javaprojects Bigdata

Academic Final year IEEE Bigdata projects hadoop 2019-2020 | Latest Final year IEEE Bigdata projects hadoop 2019-2020  in Bangalore | 2019-2020 latest IEEE Bigdata projects hadoop  | Top Final year  IEEE Bigdata projects hadoop  | CSE Final year IEEE Bigdata projects hadoop 2019-2020 in Bangalore|BE Final year IEEE Bigdata projects hadoop 2019-2020 for CSE | ME  Final year IEEE Bigdata projects hadoop 2019-2020 for Computer Science Engineering | Latest 2019-2020 Final year IEEE Bigdata projects hadoop 2019-2020.