BC4010 Data Privacy Syllabus:
BC4010 Data Privacy Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the basics of data privacy
To create architectural, algorithmic and technological foundations for the maintenance of the privacy
To become knowledgeable in Static Data Anonymization Methods.
To analyse anonymization algorithms
To understand the concept of privacy preservation
UNIT I INTRODUCTION
Data Privacy and its importance, Need for Sharing Data, Methods of Protecting Data, Importance of Balancing Data Privacy and Utility, Disclosure, Tabular Data, Micro data, Approaches to Statistical disclosure control, Ethics, principles, guidelines and regulations, Microdata concepts, Disclosure, Disclosure risk, Estimating re-identification risk, Non-perturbative microdata masking, Perturbative microdata masking, Information loss in microdata
UNIT II STATIC DATA ANONYMIZATION ON MULTIDIMENSIONAL DATA
Static Data Anonymization on Multidimensional Data, Classification of Privacy Preserving Methods, Classification of Data in a Multidimensional Data Set, Group-Based Anonymization, k-Anonymity, l-Diversity, t-closeness
UNIT III STATIC DATA ANONYMIZATION ON COMPLEX DATA STRUCTURES
Static Data Anonymization on Complex Data Structures, Privacy Preserving Graph Data, Privacy Preserving Time Series Data, Time Series Data Protection Methods, Privacy Preservation of Longitudinal Data, Privacy Preservation of Transaction Data
UNIT IV STATIC DATA ANONYMIZATION ON THREATS TO ANONYMIZED DATA
Static Data Anonymization on Threats to Anonymized Data, Threats to Data Structures, Threats by Anonymization Techniques, Randomization, k-Anonymization, l-Diversity, t-Closeness. Dynamic Data Protection: Tokenization, Understanding Tokenization, Use Cases for Dynamic Data Protection, Benefits of Tokenization Compared to Other Methods, Components for Tokenization
UNIT V PRIVACY PRESERVING
Privacy Preserving, Data Mining: Key Functional Areas of Multidimensional Data, Association Rule Mining, Clustering – Privacy Preserving Test Data Manufacturing Generation, Test Data Fundamentals, Utility of Test Data: Test Coverage, Privacy Preservation of Test Data, Quality of Test Data, Anonymization Design for PPTDG, Insufficiencies of Anonymized Test.
COURSE OUTCOMES:
CO1: Become familiar with the basics of privacy.
CO2: Understand how privacy is formalized.
CO3: Understand the common data privacy techniques.
CO4: Able to analyse Static Data Anonymization
CO5: Understand and analyse privacy preservation techniques
TOTAL: 45 PERIODS
REFERENCES
1. N. Venkataramanan and A. Shriram, “Data privacy: Principles and practice”. CRC Press, 2016. ISBN: 978-1-49-872104-2
2. A. Hundepool, J. Domingo-Ferrer, L. Franconi, S. Giessing, and E. S. Nordholt, P.D. Wolf, “Statistical disclosure control”, Wiley, John & Sons, 2012. ISBN No.: 978-1-11-997815-2
3. G. T. Duncan, M. Elliot, J.-J. Salazar-González, J.-J. Salazar-Gonzalez, and J. J. Salazar, “Statistical confidentiality: Principles and practice”, Springer-Verlag New York, 2011. ISBN: 978-1-44-197801-1
4. C. C. Aggarwal and P. S. Yu, “Privacy-preserving data mining: Models and Algorithms”, Springer-Verlag New York, 2008. (ISBN No.: 978-0-387-70992-5)