With data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

Meckels diverticulum is a persistent remnant of the vitellointestinal duct, and is one the most commonly diagnosed congenital surgical conditions. It occurs in 2% of the population, is usually two inches long, two feet proximal from the ileocaecal junction and in approximately 20% cases it is seen to contain heterotropic epithelium. Its most common clinical presentations are usually in the form of haemorrhage, diverticulitis and rarely may complicate causing intestinal obstruction. We hereby present one such case of intestinal obstruction in a young adult male, secondary to small bowel volvulus around a vitellointestinal band extending from a meckels diverticulum to the anterior abdominal wall. The presentation and management of this case, which had presented to the Emergency Department of our hospital, is elaborated in detail.

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With data becoming a salient asset worldwide, dependence within data kept on growing, hence the real world datasets that one works upon in today's time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found that where there exists a correlation among data, the existing privacy guarantees could not be assured with existing privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need, to reconsider the privacy algorithms. Some of the research have considered to utilize a well known machine learning concept, i.e., Data Correlation Analysis to understand the relationship between data in a better way. This has given some promising results as well. Though its less but still a considerable amount of research has been done for correlated data privacy. But correlated big data privacy is very less explored. The real world datasets that are worked upon, are often large in size (technologically termed as big data) and house a high amount of data correlation. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

Data are often correlated in real-world datasets. Existing data privacy algorithms did not consider data correlation an inherent property of datasets. This data correlation caused privacy leakages that most researchers left unnoticed. Such privacy leakages are often caused by homogeneity, background knowledge, and linkage attacks, and the probability of such attacks increases with the magnitude of correlation among data. This problem further got magnified by the large size of real-world datasets, and we refer to these large datasets as ’Big Data.’ Several researchers proposed algorithms using machine learning models, correlation analysis, and data privacy algorithms to prevent privacy leakages due to correlation in large-sized data. The current proposed work first analyses the correlation among data. We studied the Mutual Information Correlation analysis technique and the distance correlation analysis technique for data correlation analysis. We found out distance correlation analysis technique to be more accurate for high-dimensional data. It then divides the data into blocks using the correlation computed earlier and applies the differential privacy algorithm to ensure the data privacy expectations. The results are derived based upon multiple parameters such as data utility, mean average error, variation with data size, and privacy budget values. The results showed that the proposed methodology provides better data utility when compared to the works of other researchers. Also, the data privacy commitments offered by the proposed method are comparable to the other results. Thus, the proposed methodology gives a better data utility while maintaining the required data privacy commitments.