What are the best practices for ensuring data privacy and security in statistics?

1 Data collection and consent

Before you collect any data, you need to obtain informed consent from the participants or the data owners. This means that you need to explain the purpose, scope, and potential risks of your data collection, and respect their rights to withdraw or restrict their data. You also need to comply with any relevant ethical and legal regulations, such as the GDPR, HIPAA, or IRB guidelines. If you use secondary data sources, such as public databases or web scraping, you need to check their terms of use and cite them properly.

2 Data storage and encryption

Once you have collected your data, you need to store it securely and prevent unauthorized access or modification. You can use encryption tools, such as AES or RSA, to protect your data files and folders, and use strong passwords or biometric authentication to access them. You also need to backup your data regularly and store it in different locations, such as cloud services or external drives. If you share your data with others, you need to use secure channels, such as VPNs or SSL, and limit the access rights and duration.

4 Data reporting and dissemination

After you have analyzed your data, you need to report and disseminate your findings in a transparent and responsible way. You need to document your data sources, methods, and results, and provide any code, scripts, or software that you used. You also need to acknowledge any limitations, assumptions, or biases that may affect your interpretation or generalization of your data. If you publish your data online or in journals, you need to use open formats, such as CSV or JSON, and apply licenses, such as CC BY or CC0, that specify the terms of reuse.

5 Data retention and deletion

Finally, you need to decide how long you will keep your data and when you will delete it. You need to follow any ethical and legal obligations, such as the GDPR, HIPAA, or IRB guidelines, that specify the retention period and the deletion procedure of your data. You also need to consider the value and relevance of your data for future research or reference. If you delete your data, you need to use secure methods, such as shredding or wiping, that prevent any recovery or restoration of your data.

6 Data ethics and accountability

As a statistician, you need to adhere to some ethical principles and standards that guide your data practices and decisions. You need to respect the dignity, privacy, and autonomy of the participants or the data owners, and ensure that your data benefits them or the society. You also need to be honest, fair, and impartial in your data collection, analysis, and reporting, and avoid any conflicts of interest or misconduct. Moreover, you need to be accountable for your data actions and outcomes, and address any errors, complaints, or harms that may arise from your data.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?