Protecting Your Privacy in a Data-Driven World

The course explains modern statistical data cleaning techniques and develops tools to implement them. It also covers the logic behind modern privacy protections for researchers and the limitations imposed by these techniques.

\n Format: Paperback / softback
\n Length: 134 pages
\n Publication date: 22 November 2021
\n Publisher: Taylor & Francis Ltd
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Explains modern data stewardship techniques and develops tools to implement them.

The field of data stewardship has evolved significantly in recent years, with the rise of big data and the increasing importance of data privacy and security. As data stewards, it is our responsibility to ensure that our organization's data is used responsibly, ethically, and in compliance with all relevant laws and regulations.

One of the key techniques that modern data stewards are using is data anonymization. This involves removing personally identifiable information (PII) from data sets so that they can be used for research and analysis without compromising the privacy of individuals. There are several different methods of data anonymization, including encryption, tokenization, and hashing.

Another technique that modern data stewards are using is data minimization. This involves reducing the amount of data that is collected and stored to the minimum amount necessary to achieve the organization's goals. This can be achieved through data aggregation, which involves combining data from multiple sources into a single dataset, and data deletion, which involves removing data that is no longer needed or relevant.

Modern data stewards are also using data visualization techniques to help them understand and analyze their organization's data. This involves creating visual representations of data that can be easily interpreted and analyzed, such as charts, graphs, and dashboards. Data visualization can help data stewards identify patterns and trends in data that may not be apparent through traditional analysis methods.

In addition to these techniques, modern data stewards are also using machine learning algorithms to automate data stewardship tasks. Machine learning algorithms can be used to identify potential data breaches, analyze data to identify patterns and trends, and automate data deletion and anonymization processes.

However, it is important to note that there are also limitations to modern data stewardship techniques. For example, data anonymization and minimization can only go so far in protecting the privacy of individuals. If data is collected and stored in a way that makes it possible to identify individuals, it may still be possible to compromise their privacy. Additionally, data visualization techniques can be misleading if they are not used correctly, and machine learning algorithms can be biased if they are trained on data that is not representative of the population they are intended to serve.

To address these limitations, modern data stewards are using a variety of tools and techniques. For example, they are using data governance policies and procedures to ensure that their organization's data is used responsibly and ethically. They are also using data security measures, such as encryption and access controls, to protect their organization's data from unauthorized access and theft.

In conclusion, modern data stewardship techniques are essential for ensuring that our organization's data is used responsibly, ethically, and in compliance with all relevant laws and regulations. By using data anonymization, data minimization, data visualization, and machine learning algorithms, modern data stewards can help their organization's data be used more effectively and efficiently while also protecting the privacy of individuals. However, it is important to note that there are also limitations to these techniques, and data stewards must be aware of these limitations and take steps to address them.

\n Weight: 216g\n
Dimension: 138 x 214 x 11 (mm)\n
ISBN-13: 9780367640743\n \n