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ANONYMISATION AND PRIVACY

Module code: HM808
Credits: 5
Semester: S
Quota: 25
Department: HAMILTON INSTITUTE
International:

	Overview
	1. Basic privacy concepts 2. Deriving privacy and data protection principles from the legal framework 3. Key criteria in privacy by design 4. Privacy design strategies 5. Privacy techniques a. Authentication b. Attribute-based credentials c. Secure private communications d. Communications anonymity and pseudonymity e. Storage privacy f. Privacy-preserving computations g. Transparency-enhancing techniques h. Intervenability-enhancing techniques i. Owner privacy in databases: privacy-preserving data mining j. User privacy in databases: private information retrieval k. Respondent privacy in databases: anonymisation. 6. Private information retrieval (PIR) and relaxations a. Shortcomings of strict PIR b. Standalone PIR relaxations c. P2P PIR relaxations d. Rational behaviour in P2P PIR. 7. Anonymisation in databases a. Concepts b. Statistical databases c. Privacy models (k-anonymity, differential privacy, etc.) d. Tabular data protection e. Interactive database protection f. Microdata protection g. The permutation model of microdata protection h. Evaluation of anonymization methods i. Anonymisation software

Overview

1. Basic privacy concepts
2. Deriving privacy and data protection principles from the legal framework
3. Key criteria in privacy by design
4. Privacy design strategies
5. Privacy techniques
a. Authentication
b. Attribute-based credentials
c. Secure private communications
d. Communications anonymity and pseudonymity
e. Storage privacy
f. Privacy-preserving computations
g. Transparency-enhancing techniques
h. Intervenability-enhancing techniques
i. Owner privacy in databases: privacy-preserving data mining
j. User privacy in databases: private information retrieval
k. Respondent privacy in databases: anonymisation.
6. Private information retrieval (PIR) and relaxations
a. Shortcomings of strict PIR
b. Standalone PIR relaxations
c. P2P PIR relaxations
d. Rational behaviour in P2P PIR.
7. Anonymisation in databases
a. Concepts
b. Statistical databases
c. Privacy models (k-anonymity, differential privacy, etc.)
d. Tabular data protection
e. Interactive database protection
f. Microdata protection
g. The permutation model of microdata protection
h. Evaluation of anonymization methods
i. Anonymisation software

	Learning Outcomes
	On successful completion of the module, students should be able to: discuss the legal basis of privacy; describe the main principles of privacy by design deploy appropriate privacy techniques exist to address specific privacy problems describe some of the main data anonymisation techniques use anonymisation software

Teaching & Learning methods

Assessment

	Pre-Requisites
	Registered Research Student

	Co-Requisites
	None

	Additional Reading
	G. Danezis, J. Domingo-Ferrer, M. Hansen, J.-H. Hoepman, D. Le Métayer, R. Tirtea and S. Schiffner, Privacy and Data Protection by Design – From Policy to Engineering, European Union Agency for Network and Information Security-ENISA, 2015. A. Hundepool, J. Domingo-Ferrer, L. Franconi, S. Giessing, E. Schulte Nordholt, K. Spicer and P.-P. de Wolf, Statistical Disclosure Control, Wiley, 2012. Several research papers available from http://crises2-deim.urv.cat/articles

Additional Reading

	Timetable
	This timetable is not currently available, please contact the Hamilton Institute Department.

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Courses / Module

ANONYMISATION AND PRIVACY