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Presentation
Presentation
This curricular unit aims to provide knowledge and skills in the fields of ethics, security and privacy in data science, as well as to promote critical analysis and impact assessment for the use of advanced technologies and big data.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6347-25228
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
S1. Introduction to Ethics. Utilitarianism and deontology.
S2 Ethics and data science. Ethical considerations involved in the algorithmic processing of sensitive data and the responsible development of Artificial Intelligence.
S3. Data management and data lineage. Sensitive information and personal information: how to identify it.
S4. Privacy technologies. Basic privacy techniques in data science. Anonymization and pseudo-anonymization. Differential privacy.
S5. Legal and regulatory frameworks related to data privacy and security.
S6. Introduction to general security concepts and their importance in machine learning systems.
S7. Types of attacks on machine learning systems
S8. Mitigation approaches, regulations and security guidelines for machine learning systems
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Objectives
Objectives
LG1. Understanding the foundations of Ethics as a moral philosophy and thinking about a deontology for data science.
LG2. Understanding the fundamental protocols in data management and the concept of data lineage.
LG3. Identifying sensitive information and personally identifiable information.
LG4. Understanding specific techniques that can help protect individual privacy when working with large data sets.
LG5. Understanding the legal and regulatory requirements related to data privacy and security.
LG6. Understanding the importance of establishing robust data management structures within organizations to ensure compliance with privacy regulations.
LG7. Understanding the need for specific security measures for machine learning systems.
LG8. Understanding the main types of attacks on machine learning systems and the measures to prevent and mitigate them.
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Teaching methodologies and assessment
Teaching methodologies and assessment
The lectures are conducted in person and are primarily based on exposition. The content is illustrated with examples and detailed case studies. Students will be asked to participate actively by presenting cases or concepts, both technical and theoretical. Students will be encouraged to intervene continuously in class, particularly after the formal interventions of their classmates.
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References
References
- Jarmul, K. (2023). Practical Data Privacy. O'Reilly Media, Inc.
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Office Hours
Office Hours
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Mobility
Mobility
No
