IDENTIFYING DATA

2019_20

Subject (*)

PRIVACY PROTECTION

Code

17685107

Study programme

Computer Security Engineering and Artificial Intelligence (2016)

Cycle

2nd

Descriptors

Credits

Type

Year

Period

4.5

Compulsory

First

2Q

Language

Anglès

Department

Computer Engineering and Mathematics

Coordinator

DOMINGO FERRER, JOSEP

E-mail

Lecturers

Web

http://http://crises-deim.urv.cat/privprot

General description and relevant information

General description Learn the basics of privacy, its legal principles and major design strategies . Gain expertise in database privacy and data anonymization.

Competences

Type A	Code	Competences Specific
	A1	Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
	A3	Understand and know how to apply the functioning and organisation of the Internet, the technology and protocols of new-generation networks, the models of components, intermediate software and services.
	A4	Design, develop, manage and evaluate mechanisms to certify and guarantee security in handling information and access to it in a local or distributed processing system.
	A5	Analyse the information needs considered in an environment and execute all stages of the construction process of a secure information system.
	G1	Project, calculate and design products, processes and installations in the areas of Computer Security and Artificial Intelligence
	G2	Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
Type B	Code	Competences Transversal
	CT2	Forming opinions on the basis of the efficient management and use of information
	CT3	Solve complex problems critically, creatively and innovatively in multidisciplinary contexts.
	CT4	Work in multidisciplinary teams and in complex contexts.
	CT7	Apply ethical principles and social responsibility as a citizen and a professional.
Type C	Code	Competences Nuclear

Learning outcomes

Type A	Code	Learning outcomes
	A1	Analyse the problems and their causes from a global focus in the medium and long term.
	A3	Design network protocols and private services for computer and telematic applications.
	A4	Design technology to guarantee privacy for scenarios of IT and telematics applications.
	A5	Identify the components of a decision-making problem and know how to decide the most suitable decision-making model.
	G1	Integrate theoretical knowledge into the realities to which it may apply.
	G2	Apply the techniques learned in a specific context.
Type B	Code	Learning outcomes
	CT2	Manage information and knowledge by making efficient use of the information technologies.
	CT3	Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
	CT4	Participate in the group in a good working environment and help to solve problems.
	CT7	Apply ethical and socially responsible principles as a citizen and a professional.
Type C	Code	Learning outcomes

Contents

Topic	Sub-topic
1. Introduction	a. Basic concepts of privacy b. Legal principles c. Privacy by design d. Design strategies for privacy
2. Data privacy techniques	a. Authentication b. Attribute-based Credentials. c. Secure and private communications d. Anonymity and pseudo-anonymity in communications. e. Privacy in data storages f. Privacy-preserving computations g. Techniques for improving transparency h. Intervenability-enhancing techniques
3. Privacy in data bases	3. Privacy in data bases a. Owner's privacy del propietari (Privacy-preserving data mining) b. User's privacy (private information retrieval) c. Respondent's privacy (anonymization)
4. User's privacy	a. Issues of private information retrieval (PIR). b. Modifications to PIR based on single users. c. Modifications to PIR based on p2p networks (P2P PIR). d. Rational behaviour in P2P PIR.
5. Anonymization in data bases	a. Basic concepts b. Privacy models c. Protection of tables d. Protection of interactive data bases. e. Protection of microdata g. Evaluation of statistical disclosure control methods. h. Anonymization software

Planning

Methodologies :: Tests

Competences

(*) Class hours

Hours outside the classroom

(**) Total hours

Introductory activities

1

1.5

2.5

Presentations / oral communications

1

1.5

2.5

Reading written documents and graphs

26

37.5

63.5

Forums of debate

1

1.5

2.5

Assignments

13

19.5

32.5

Personal attention

1

0

1

(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

	Description
Introductory activities	Dissemination of news on privacy in the media.
Presentations / oral communications	Individual preparation of presentations on specific subjects proposed by the teacher.
Reading written documents and graphs	Reading scientific or technical articles.
Forums of debate	Debating hot technical issues in privacy technologies.
Assignments	Implementing privacy technologies in software.
Personal attention	Individual attention in agreed hours.

Personalized attention

Description

Students can request via e-mail as many interviews as needed with the teacher. Interviews will be by skype or a similar system.

Assessment

Methodologies

Competences

Description

Weight

Presentations / oral communications

Individual preparation of presentations on specific subjects proposed by the teacher.

20%

Assignments

Individual implementation of a privacy-enhancing technology.

60%

Others

Individual exam (remote)

20%


Other comments and second exam session

Sources of information

Basic
	o G. D’Acquisto, J. Domingo-Ferrer, P. Kikiras, V. Torra, Y.-A. De Montjoye and A. Bourka (2015) Privacy by Design in Big Data – An overview of privacy enhancing technologies in the era of big data analytics, European Union Agency for Network and Information Security-ENISA. o G. Danezis, J. Domingo-Ferrer, M. Hansen, J.-H. Hoepman, D. Le Métayer, R. Tirtea and S. Schiffner (2015) Privacy and Data Protection by Design: From Policy to Engineering, European Union Agency for Network and Information Security-ENISA. o J. Domingo-Ferrer, D. Sánchez and J. Soria-Comas (2016) Database Anonymization: Privacy Models, Data Utility and Microaggregation-Based Inter-Model Connections, Morgan & Claypool. o A. Hundepool, J. Domingo-Ferrer, L. Franconi, S. Giessing, E. Schulte-Nordholt, K. Spicer and P.-P. de Wolf (2012) Statistical Disclosure Control, Wiley.
Complementary

Recommendations

(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.