DATA PRIVACY MTCSPE1001D
Course objectives:
1. To create architectural, algorithmic and technological foundations for the maintenance of the privacy of individuals, the confidentiality of organizations, and the protection of sensitive information, despite the requirement that information be released publicly or semi-publicly.
Course outcomes: After successful completion of this course, students will be able to:
1. Understand the concepts of privacy in today’s environment.
2. Obtain the understanding of how automation is changing the concepts and expectations concerning privacy and the increasingly interconnected issue of security.
3. Obtain the knowledge of the role of private regulatory and self-help efforts.
4. Have an understanding of how emerging issues are affecting society and business, with a concentration on how information security must shape corporate practices.
Course Contents:
UNIT I
Introduction- Fundamental Concepts, Definitions, Statistics, Data Privacy Attacks, Data linking and profiling, access control models, role-based access control, privacy policies, their specifications, languages and implementation, privacy policy languages, privacy in different domains- medical, financial, etc.
UNIT II
Data explosion- Statistics and Lack of barriers in Collection and Distribution of Person- specific information. Mathematical model for characterizing and comparing real-world data sharing practices and policies and for computing privacy and risk measurements, Demographics and Uniqueness.
UNIT III
Protection Models- Null-map, k-map, Wrong map Survey of techniques- Protection models (null-map, k-map, wrong map), Disclosure control, Inferring entity identities, Strength and weaknesses of techniques, entry specific databases. Computation systems for protecting delimited data- MinGen, Datafly, Mu-Argus, k-Similar, Protecting textual documents: Scrub.
UNIT IV
Technology, Policy, Privacy and Freedom- Medical privacy legislation, policies and best practices
UNIT V
Examination of privacy matters specific to the World Wide Web, Protections provided by the Freedom of Information Act or the requirement for search warrants.
References:
1. B. Raghunathan, The Complete Book of Data Anonymization: From Planning to Implementation, Auerbach Pub, 2013.
2. L. Sweeney, Computational Disclosure Control: A Primer on Data Privacy Protection, MIT Computer Science, 2002.
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