Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user’s information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.

Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective.

In the current decade, doing the search on massive data to find “hidden” and valuable information within it is growing. This search can result in heavy processing on considerable data, leading to the development of solutions to process such huge information based on distributed and parallel processing. Among all the parallel programming models, one that gains a lot of popularity is MapReduce. The goal of this paper is to survey researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level. We managed to do a systematic review based on the prevalent topics dealing with MapReduce in seven areas: (1) performance; (2) job/task scheduling; (3) load balancing; (4) resource provisioning; (5) fault tolerance in terms of availability and reliability; (6) security; and (7) energy efficiency. We run our study by doing a quantitative and qualitative evaluation of the research publications’ trend which is published between January 1, 2014, and November 1, 2017. Since the MapReduce is a challenge-prone area for researchers who fall off to work and extend with, this work is a useful guideline for getting feedback and starting research.

MapReduce: an infrastructure review and research insights

Nowadays, healthcare and life sciences overall have produced massive amounts of real-time data by enterprise resource planning (ERP). These large of amount data is a difficult task to handle, and intimidation of data leakage by inside worker rises, the firms are smearing way out for security such as Data Loss Prevention (DLP) and Digital Rights Management (DRM) to prevent data leakage. On the other hand, data leakage system also turns into varied and challenging to avert data leakage. Machine learning techniques are used for the handling of significant data by evolving algorithms and set of rules to provide the prerequisite results to the workers. Deep learning has automatic feature extraction that grasps the essential features necessary for the solution of the problem. It reduces the issue of the workers to select elements explicitly to solve the problems for supervised, unsupervised and semi-supervised for healthcare data’s.

https://www.jetir.org/papers/JETIRC006413.pdf

Data Wrangling and Data Leakage in Machine Learning for Healthcare

In recent years, data has become the new oil that fuels various machine learning (ML) applications. Just as the oil refining, providing data to an ML model is a product of massive costs and expertise efforts. However, how to protect the intellectual property (IP) of the training data in ML remains largely open. In this paper, we present MeFA, a novel framework for detecting training data IP embezzlement via Membership Fingerprint Authentication, which is able to determine whether a suspect ML model is trained on the to be protected target data or not. The key observation is that a part of data has a similar influence on the prediction behavior of different ML models. On this basis, MeFA leverages membership inference techniques to extract these data as the fingerprints of the target data and constructs an authentication model to verify the data’s ownership by identifying the obtained membership fingerprints. MeFA has several salient features. It does not assume any knowledge of the suspect model except for its black-box prediction API, through which we can merely get the prediction output of a given input, and also does not require any modification to the dataset or the training process, since it takes advantage of the inherent membership property of the data. As a by-product, MeFA can also serve as a post-protection to verify the ownership of ML models, without modifying the training process of the model. Extensive experiments on three realistic datasets and seven types of ML models validate the effectiveness of MeFA, and demonstrate that it is also robust to scenarios when the training data is partially used or preprocessed with representative membership inference defenses.

Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication

Big data is the core topic in industries and research fields as well as for society as a whole. Analytics of Big Data is a predictive analysis rather than traditional descriptive analysis of data. Hadoop is the most widely used tool for big data analytics in social media like Google, Facebook, Yahoo, Amazon etc. Hadoop basically uses Distributed File System for the storage of large volume of unstructured, real time data and streams at a high velocity. It has given precise importance to data storage in Hadoop, but the security of data has ignored and very least importance was given. We have a review of algorithms or methodologies suggested.

Hadoop Distributed File System Security -A Review

As one of the most popular platforms for processing big data, Hadoop has low costs, convenience, and fast speed. However, it is also a significant target of data leakage attacks, as a growing number of businesses and individuals store and process their private data in it. How to investigate data leakage attacks in Hadoop is an important but long-neglected issue. This article first presents some possible data leakage attacks in Hadoop. Then an investigation framework is proposed and tested based on some simulated cases.

Yun Gao

Papers

Security Threats to Hadoop: Data Leakage Attacks and Investigation