Cloud Computing is referred to as a set of hardware and software that are being combined to deliver various services of computing. The cloud keeps the services for delivery of software, infrastructure, and platform over the Internet based on the user’s demand. In the IT industry, cloud computing plays an important role to access services anywhere in the world. With increasing demand and popularity of cloud computing, several types of threats and vulnerabilities are also increased. Data integrity and privacy are the key issues in cloud computing and are thoughtful as the data is stored in different geographical locations. Therefore, data integrity and privacy protection provisions are the most prominent factors of user’s concerns about the cloud computing environment. In this paper, a new model based on a genetic algorithm (GA) CryptoGA is proposed to cope with data integrity and privacy issues. GA is used to generate keys for encryption and decryption which are integrated with a cryptographic algorithm to ensure privacy and integrity of cloud data. Known and common parameters i.e. execution time, throughput, key size, and avalanche effect are considered for evaluation and comparison. Ten different datasets are used in experiments for testing and validation. Experimental results analysis show that the proposed model ensures the integrity and preserves the privacy of the user’s data against unauthorized parties. Moreover, the CryptoGA is robust and provides better performance on selected parameters as compared to state-of-the-art cryptographic algorithms i.e. DES, 3DES, RSA, Blowfish, and AES.

CryptoGA: a cryptosystem based on genetic algorithm for cloud data security

Abstract Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models. 

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Natural language processing applied to mental illness detection: a narrative review

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

/pdf/a-robust-blind-medical-image-watermarking-approach-for-4h7tkn8d29.pdf

A robust blind medical image watermarking approach for telemedicine applications

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

Secure video communication using firefly optimization and visual cryptography

Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization

Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they ﬁnd it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress ﬁnd it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efﬁcient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.

/pdf/detecting-the-magnitude-of-depression-in-twitter-users-using-22wpypm0n3.pdf

Detecting the magnitude of depression in Twitter users using sentiment analysis

Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

https://www.techscience.com/cmc/v70n2/44640/pdf

Cross-Layer Hidden Markov Analysis for Intrusion Detection

Nowadays there are numerous intruders trying to get the privacy information from cloud resources and consequently need a high security to secure our data. Moreover, research concerns have various security standards to secure the data using data hiding. In order to maintain the privacy and security in the cloud and big data processing, the recent crypto policy domain combines key policy encryption with reversible data hiding (RDH) techniques. However in this approach, the data is directly embedded resulting in errors during data extraction and image recovery due to reserve leakage of data. Hence, a novel shuffle block key encryption with RDH technique is proposed to hide the data competently. RDH is applied to encrypted images by which the data and the protection image can be appropriately recovered with histogram bit shifting algorithm. The hidden data can be embedded with shuffle key in the form of text with the image. The proposed method generates the room space to hide data with random shuffle after encrypting image using the definite encryption key. The data hider reversibly hides the data, whether text or image using data hiding key with histogram shifted values. If the requestor has both the embedding and encryption keys, can excerpt the secret data and effortlessly extract the original image using the spread source decoding. The proposed technique overcomes the data loss errors competently with two seed keys and also the projected shuffle state RDH procedure used in histogram shifting enhances security hidden policy. The results show that the proposed method outperforms the existing approaches by effectively recovering the hidden data and cover image without any errors, also scales well for large amount of data.

P. Prabu

Papers

Detecting the magnitude of depression in Twitter users using sentiment analysis

Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

Cross-Layer Hidden Markov Analysis for Intrusion Detection

An enhancing reversible data hiding for secured data using shuffle block key encryption and histogram bit shifting in cloud environment

Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT