Besides that, it utilizes the correlation coefficient to characterize the correlation among features, and allow the tool users (i.e., the data managers) to directly observe the effects of the combination of features and sampling rates to better understand the utility and privacy information implied by the data to be shared.
We are interested in LD in relation to genetic association studies, in which the redundancy of information implied by LD can be used to optimize genotyping.
A strong LD might be also a common feature of a biallelic CNV, which is particularly useful in association studies for complex disorders in which the redundancy of information implied by LD can be used to optimize genotyping.
It can help people understand the information implied in time series and support people to make the right decision.
Previous studies [44, 45] have shown that the classification type information implied by a feature can be used to evaluate the usefulness of a feature.
Through realizing the above goals/tasks, the visualization tool proposed in this work provides the opportunities for users to analyze the activity/identity information implied by different combinations of features and sampling rates.
Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data.
This module provides to users only the top combinations in terms of texts, according to the utility and privacy information implied by different combinations.
The results of the present study further suggest that size information, implied by specific sound categories, contributes additional memorability to nonword-definition pairings.
To include users' preferences into privacy-preserving data sharing, an interactive visualization tool is designed to enable users to observe and analyze the utility and privacy information implied by the combinations of features and sampling rates.
