Introduction to Fides Lang with Cillian Kieran

Summary

Kilian Kiernan introduces Fideslang, a high-level description language for privacy that simplifies complex privacy tasks by integrating with CI pipelines and runtime environments. Fideslang uses a standard ontology to define privacy characteristics of software systems and data, focusing on data categories, uses, subjects, and qualifiers. This language is designed to be human-readable, making it accessible for developers and non-engineers alike, ensuring privacy compliance and interoperability across systems.

Takeaways

• 📝 Fideslang is a high-level description language for privacy, designed to simplify and automate complex privacy tasks.
• 🌐 It integrates with CI pipelines and runtime environments, supporting privacy in software systems and data processing.
• 🔒 Privacy is complex due to varying global regulations and a lack of interoperability in data definitions across teams and companies.
• 👨‍🏫 Developers often require significant training to understand privacy concepts, highlighting the need for standardized privacy ontologies.
• 🗂️ Fideslang uses a taxonomy that includes four major privacy primitives: data categories, data uses, data subjects, and data qualifiers.
• 🔑 Data categories define the type of data processed, such as 'contact data' or 'email address'.
• 🎯 Data uses label the purpose of data usage, like 'advertising' or 'personalization'.
• 👤 Data subjects represent the type of user or subject whose data is being processed, with different rights and policies.
• 🔍 Data qualifiers denote the degree of identification in the data, such as 'aggregated', 'anonymized', or 'identifiable'.
• 📖 Fideslang is designed to be easy to read, understand, and write, making it accessible to developers and non-engineers alike.
• 🔗 It provides a clear understanding of privacy characteristics, behaviors, and risks associated with data processing in systems.
• 💡 Fideslang's hierarchical taxonomy and dot notation allow for precise and fine-grained privacy definitions, enhancing compliance and interoperability.

Q & A

What is Fideslang and how does it relate to privacy?

Fideslang is a high-level description language for privacy, supported by a set of tools and workflows. It integrates with CI pipelines and runtime environments to simplify and automate complex privacy tasks, helping teams manage privacy in compliance with global regulations.

Why is privacy a complex problem in software development?

Privacy is complex due to multiple global regulations with different definitions for how specific categories of data should be treated, compounded by a lack of interoperability as every team and company may define their own view of data types.

What does Fideslang aim to achieve by defining a standard ontology for privacy?

Fideslang aims to achieve a clear understanding of privacy characteristics, behaviors, and associated risks of systems' data processing by defining a standard ontology, making it easier for developers and non-engineers to understand and comply with privacy regulations.

What are the four major privacy primitives captured by Fideslang's taxonomy?

The four major privacy primitives are data categories, data uses, data subjects, and data qualifiers. These primitives help describe the type of data, the purpose of its use, the type of user or subject it pertains to, and the degree of identification it provides.

How does Fideslang's hierarchical taxonomy work?

Fideslang's taxonomy is hierarchical, allowing for both broad and fine-grained categorization of data. For example, 'contact data' is a broad category that includes all contact information, while 'email address' is a more specific sub-category within it.

What is the purpose of data categories in Fideslang?

Data categories in Fideslang represent the types of data a system processes, providing a clear 'what' aspect of the data being handled, which is crucial for understanding privacy implications.

Can you provide an example of how data uses are described in Fideslang?

Data uses in Fideslang are described using labels that indicate the purpose for which data is used, such as 'advertising' or 'personalization'. This provides a 'why' aspect, helping to clarify the intent behind data processing.

Why is it important to distinguish data subjects in privacy regulations?

Data subjects represent the type of user or subject whose data is being processed. Distinguishing them is important because rights or policies for data processing may vary by subject grouping, such as a customer versus a patient.

What do data qualifiers in Fideslang signify?

Data qualifiers in Fideslang denote the degree of identification for a given data, indicating how identifiable the individual is. Qualifiers include 'aggregated', 'anonymized', and 'identifiable', which help define the privacy level of the data.

How does Fideslang make it easier to comply with privacy regulations?

Fideslang provides an easy-to-read, understand, and write definition language for privacy that synthesizes major regulations. This ensures that rules and policies can be applied evenly across systems and provides an interoperable standard for privacy.

Where can one find more information about Fideslang and its technical documentation?

For more information about Fideslang, one can visit ethica.com or check the detailed technical documentation located on the GitHub repository for Fideslang.