How deeply does Effective Data Analysis cover sensitive information?

Defines major data categories and gives handling practices, consent questions, and re-identification cautions without a technical security implementation.

Sensitive information

As explained in Effective Data Analysis

Protected information is data subject to legal, regulatory, or organizational restrictions governing its collection, use, disclosure, or retention.

Sensitive information is data about people or organizations that could cause harm if disclosed without consent or authorization. Some of it is also protected by laws governing how it is collected, used, stored, and deleted. In analytics, the first question is not how to secure every available field, but whether the analysis needs that field at all. section 8.3 section 8.3.1

Common categories call for different kinds of care:

Personally identifiable information (PII), such as names, phone numbers, email addresses, and other identifying characteristics, can link records to an individual. section 8.3
Protected health information (PHI) includes diagnoses, medical history, and payment information that can be tied to a person. section 8.3
Financial information includes accounts, credit card numbers, and transactions; authentication data includes login credentials, whose exposure can put other data at risk. section 8.3

Data protection regulations may require limited collection and retention, restricted access, and ways for people to learn how their data is used, deny consent, or request deletion. Work with security, IT, and legal teams where available. Collect only what serves a defined purpose, set a retention period, and avoid sending raw records to unauthorized stakeholders or through insecure channels. section 8.2.1 section 8.3.1

A wearable-device health study

A study combining activity data with participants’ heart-disease or hypertension diagnoses needs more than a useful research question. Its consent protocol should explain possible harms and benefits, its handling practices should address the risk of disclosure, and its team should be ready to honor a participant’s request to withdraw consent. section 8.1.3

Removing direct identifiers can reduce exposure, but do not assume a dataset is safe simply because it is labeled anonymized. Pseudonymization replaces identifying information with a key while retaining a way to reconnect records; even anonymized data can be re-associated with individuals. Establish guardrails against re-identification, and consider downstream harms as well as breaches: an automated hiring model trained on biased resumes, for example, penalized women applicants. section 8.4 section 8.0

How Effective Data Analysis covers it

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Also explained in

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The Art of AI Product DevelopmentInformation owned or controlled by an organization and not generally available for unrestricted public use.
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Generative AI in ActionEnterprise data is organization-owned information that may be subject to access restrictions, privacy requirements, and legal obligations.
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Introduction to Generative AI, Second EditionProprietary data is information owned or controlled by an organization and not generally available to the public.
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Practical Automation with PowerShell: Effective scripting from the console to the cloudSensitive data is information whose unauthorized disclosure, alteration, or loss could cause harm or compromise security.
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Think Like a CTOSensitive data is information requiring additional protection because unauthorized access or disclosure could cause harm or violate obligations.
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ASP.NET Core in Action, 3eSensitive data is information whose disclosure or misuse could harm a person, system, or organization.
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Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and TerraformA sensitive value is configuration or state data whose disclosure could compromise security and is therefore specially protected in displayed or stored form.

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Related concepts

personal dataincludes · Personal data is information relating to an identified or identifiable person, directly or indirectly, alone or in combination with other information.
fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
password authenticationincludes · Password authentication is an authentication method that verifies a claimed identity or access by checking a supplied secret password or passphrase.
retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.