The management of data while maintaining its utility and preservation of security scheme is a matter of concern for the cloud owner. In order to minimize the overhead at cloud service provider of applying security over each document and then send it to the client, we propose a layered architecture. This approach maintains security of the sensitive document and privacy of its data sensitivity. To make a balance between data security and utility, the proposed approach categorizes the data according to its sensitivity. Perseverance of various categorization requires different algorithmic schemes. We set up a cloud distributed environment where data is categorized into four levels of sensitivity; public, confidential, secret, top secret and a different approach has been used to preserve the security at each level. At the most sensitive layers i.e. secret and top secret data, we made a provision to detect the faulty node that is responsible for data leakage. Finally, experimental analysis is carried out to analyze the performance of the layered approach. The experimental results show that time taken (in ms) in processing 200 documents of size 20 MB is 437, 2239, 3142, 3900 for public, confidential, secret and top secret data respectively when the documents are distributed among distinct users, which proves the practicality of the proposed approach.

/pdf/layer-based-privacy-and-security-architecture-for-cloud-data-4pnh9o3m4p.pdf

Layer-based privacy and security architecture for cloud data sharing

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented. Afterward, the applicability of the techniques is discussed as per the requirements and the research gaps along with future directions are reported in the field. The authors believe that this article’s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area.

/pdf/secure-data-storage-and-sharing-techniques-for-data-3np3fd5n.pdf

Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions

Data sharing across multiple different entities is on-demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long-term stability. This study presents a novel model GUIM-SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D-score and U-score that are assigned to the classified data and user, correspondingly. Furthermore, D-score and U-score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ifs.2019.0203

GUIM-SMD: guilty user identification model using summation matrix-based distribution

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System's analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection.

A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions

This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM-1 takes date, time, and temperature as input, and DNNM-2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM-3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM-3) outperformed the other models with better accuracy as compared to past evidence. 

Deep learning model for temperature prediction: A case study in New Delhi

In the field of wireless networks, Mobile Ad hoc Network (MANET) is one of the most unique applications due to mobility and scalability. As it is vulnerable to malicious attackers with the open medium and wide distribution, network performance is degraded with the behavior attacks in MANET’s, which makes protection most important. In any ad hoc networks, one of the situations is that, when a data distributor possesses sensitive data destined to a set of supposedly trusted parties, some data can be leaked at an authorized place. With the introduction of fake objects the data allocation strategies gives a path in identifying leakages ensuring more security with encryption. In this context, light weight system is proposed to characterize the data loss and to minimize the performance degradation in such networks. The light weight system includes the concept of cryptography and routing protocol implementation at different stages of data transfer. A social network is taken as a challenging issue to achieve the proposed scheme.

An Approach to Identify Data Leakage in Secure Communication

Improving The Predictive Response Using Ensemble Empirical Mode Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network

Data everywhere should possess some level of se-curity in a network or a host based system. This is an impor-tant task that needs to be implemented with the global reach standards. This scenario basically happens to be started with communication, which itself is a tedious issue sometimes over a network. The global reach is headed by intruders where the need for survey is required. In this regard, the concept of security with network data and traffic is to be adopted. Basically, this paper focuses to estimate network traffic and its behavior and finally concludes with identification of attacks. It is obvious that data is voluminous to analyze and predict attacks. This enabled to show images of detection, identification and visualize attacks and anomalous traffic in real-time by passively monitoring packet headers.

An approach to identify anomaly with network data analysis

As the popularity of cloud services grows, more and more users choose to store their data on cloud servers to facilitate them, along with a reduction in data management costs. However, data owners want to encrypt sensitive data before sending it to cloud servers, which makes it impossible to perform keyword-based search. To work around this, a search scheme is proposed that works with encrypted cloud data that uses keywords for indexing and searching for queries. The scheme constructs an index by analyzing the data uploaded by the data owner. This index is used for searching over a query and retrieves the relevant documents. The scheme also provides "Fuzzy Search" capabilities along with an exact word search. This scheme enables users to upload encrypted data to the cloud and also provide them with search capabilities without showing any of the data or the query.

An efficient approach to perform Multi-Fuzzy Keyword Search over encrypted data in Cloud Computing

The power electronic interface is critical in matching a distributed generation (DG) unit’s characteristics to grid requirements as most DG technologies rely on renewable energy. Increased adoption of electric vehicles (EV) is seen as a positive step toward minimizing air pollution as well as carbon emissions. Rapid proliferation of electric vehicles as well as charging stations has exacerbated voltage quality as well as harmonic distortion difficulties, which harm the efficiency of combined renewable energy. This research proposes novel hybrid design techniques in control systems that enhance the energy efficiency of electronic converters for power electronics. The control system enhancement has been carried out using a hybrid energy storage electric convertor, and energy efficiency is improved using a synergetic battery reference adaptive controller. A plug-in hybrid electric vehicle (PHEV)’s internal combustion engine with a small photovoltaic (PV) module is utilised to assess a proposed control method which effectively regulates electric power on-grid by draining electricity from batteries during peak hours as well as then charging them during off-peak times, lowering the load on the converter as well as allowing electric vehicles to charge faster. Experimental results show the constant acceleration case obtained battery current of 92 Amps, ultracapacitor current of 89 Amps, charging voltage of 88 V, DC load current of 85 Amps, battery SOC of 72%, and the time-varying acceleration proposed technique obtained current of 94 Amps, and ultracapacitor current of 90 Amps, charging voltage of 90 V, DC load current of 82 Amps, battery SOC of 79%.

Renewable Energy Based Smart Grid Construction Using Hybrid Design in Control System with Enhancing of Energy Efficiency of Electronic Converters for Power Electronic in Electric Vehicles

Suhasini Sodagudi

Papers

An Approach to Identify Data Leakage in Secure Communication

Improving The Predictive Response Using Ensemble Empirical Mode Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network

An approach to identify anomaly with network data analysis

An efficient approach to perform Multi-Fuzzy Keyword Search over encrypted data in Cloud Computing

Renewable Energy Based Smart Grid Construction Using Hybrid Design in Control System with Enhancing of Energy Efficiency of Electronic Converters for Power Electronic in Electric Vehicles