Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2021/2022

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
Primo semestre Oct 4, 2021 Jan 28, 2022
Secondo semestre Mar 7, 2022 Jun 10, 2022
Exam sessions
Session From To
Sessione invernale d'esame Jan 31, 2022 Mar 4, 2022
Sessione estiva d'esame Jun 13, 2022 Jul 29, 2022
Sessione autunnale d'esame Sep 1, 2022 Sep 29, 2022
Degree sessions
Session From To
Sessione di laurea estiva Jul 20, 2022 Jul 20, 2022
Sessione di laurea autunnale Oct 19, 2022 Oct 19, 2022
Sessione invernale Mar 15, 2023 Mar 15, 2023
Holidays
Period From To
Festa di Tutti i Santi Nov 1, 2021 Nov 1, 2021
Festa dell'Immacolata Dec 8, 2021 Dec 8, 2021
Festività natalizie Dec 24, 2021 Jan 2, 2022
Vacanze Pasquali Apr 15, 2022 Apr 19, 2022
FESTA DEL LAVORO May 1, 2022 May 1, 2022
Santo Patrono May 21, 2022 May 21, 2022
Festa della Repubblica Jun 2, 2022 Jun 2, 2022
Chiusura estiva Aug 15, 2022 Aug 20, 2022

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D F G I M O P Q S V Z

Albi Giacomo

giacomo.albi@univr.it +39 045 802 7913

Badino Massimiliano

massimiliano.badino@univr.it +39 045 802 8459

Bazzani Claudia

claudia.bazzani@univr.it 0458028734

Begalli Diego

diego.begalli@univr.it +39 045 8028731

Boscolo Galazzo Ilaria

ilaria.boscologalazzo@univr.it +39 045 8127804

Carra Damiano

damiano.carra@univr.it +39 045 802 7059

Carradore Marco

marco.carradore@univr.it

Castellini Alberto

alberto.castellini@univr.it +39 045 802 7908

Ceccato Mariano

mariano.ceccato@univr.it

Chiarini Andrea

andrea.chiarini@univr.it 045 802 8223

Cobelli Nicola

nicola.cobelli@univr.it 0458028295

Confente Ilenia

ilenia.confente@univr.it 045 802 8174

Dai Pra Paolo

paolo.daipra@univr.it +39 0458027093

Dalla Preda Mila

mila.dallapreda@univr.it

Di Persio Luca

luca.dipersio@univr.it +39 045 802 7968

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Gaudenzi Barbara

barbara.gaudenzi@univr.it 045 802 8623

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Guerra Giorgia

giorgia.guerra@univr.it

Marastoni Niccolo'

niccolo.marastoni@univr.it

Mola Lapo

lapo.mola@univr.it 045/8028565

Owusu Abigail

abigail.owusu@univr.it

Paci Federica Maria Francesca

federicamariafrancesca.paci@univr.it +39 045 802 7909

Pelgreffi Igor

igor.pelgreffi@univr.it

Quintarelli Elisa

elisa.quintarelli@univr.it +39 045 802 7852

Setti Francesco

francesco.setti@univr.it +39 045 802 7804

Zardini Alessandro

alessandro.zardini@univr.it 045 802 8565

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

ModulesCreditsTAFSSD
ModulesCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-

1° Year

ModulesCreditsTAFSSD

2° Year

ModulesCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
6
C
(IUS/17)
6
C
(SPS/07)
Between the years: 1°- 2°2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
Between the years: 1°- 2°2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
Between the years: 1°- 2°2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
Between the years: 1°- 2°

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S009066

Coordinatore

Mila Dalla Preda

Credits

6

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Language

English en

Period

Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.

Learning outcomes

The course aims to provide students with an introduction to the main security and privacy issues related to the collection, storage and processing of Big Data and the technical and organizational solutions that can be adopted to protect such data. The course also aims to give an overview of the ethical, legal and social aspects related to the processing of Big Data. At the end of the course the student has to show to have acquired the following skills: ▪understanding of the main security and privacy attacks on Big Data ▪ knowledge of the techniques to make systems for collecting, storing and processing Big Data, resistant to such attacks and the limitations of these techniques ▪ knowledge of the ethical principles concerning the processing of Big Data ▪ knowledge of the principles for data protection imposed by existing legislation ▪ ability to identify the main attacks and compare different techniques for Big Data protection and choose among the most suitable ones according to the a-specific context.

Program

The syllabus of the course includes the following topics:
- Introduction to information security: definitions, security properties, cyber attacks related to collection, storage and processing of Big Data
- Authentication: digital certificates, public key infrastructures, single sign on, challenge-response protocols.
- Access Control: access control models, specification and enforcement of policies. Applications to systems
for the elaboration of Big Data
-Cryptographic techniques to protect data access: symmetric, e public key cryptography, multiparty computation, secret sharing schemes, oblivious transfer, homomorphic and functional encryption, private set intersection.
- Data provenance: models to represent data provenance, query languages and mechanisms to store and visualize provenance data and their application to Big Data
- Introduction to Privacy: definitions, Solove's Taxonomy, privacy attacks related to collection, storage and processing of Big Data
-Anonymization techniques: pseudoanonymity and hashing, k-anonymity, l-diversity, t-closeness and their attacks. Limitations of anonymization techniquest for Big Data.
- Privacy preserving data mining: clustering, classification, association rule/pattern mining, outliers.
- Differential Privacy: main concepts, Laplace mechanism, privacy budget, global sensitivity, group privacy.
- Privacy Ethics: behavioural economics of privacy, trust frameworks and transparency, fairness.
- Data Protection: principles of data protection, GDPR, compliance techniques.

Examination Methods

Students will be assessed through a project /assigned to them in agreement with the teachers on the topics of the course. The results of the project will then be presented orally to the teachers who will be able to request and deepen the student's training on the various topics covered during the course.

Type D and Type F activities

Le attività formative di tipologia D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite. Dal 1° dicembre 2021 al 27 febbraio 2022 e dal 2 maggio 2022 al 15 luglio 2022, tramite il presente modulo gli studenti possono richiedere l'inserimento di attività didattiche in TAF D ed F che non possono inserire autonomamente nel proprio piano di studi tramite la procedura on-line.

COMPETENZE LINGUISTICHE - dal 1° ottobre 2021 (Delibera del Consiglio della Scuola di Scienze e Ingegneria del 30 marzo 2021) per gli immatricolati dall'A.A. 2021/2022

  • Lingua inglese: vengono riconosciuti automaticamente 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
  • Altre lingue e italiano per stranieri: vengono riconosciuti automaticamente 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).
Tali CFU saranno riconosciuti, fino ad un massimo di 6 CFU complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D.
Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.

COMPETENZE TRASVERSALI
Scopri i percorsi formativi promossi dal  Teaching and learning centre dell'Ateneo, destinati agli studenti iscritti ai corsi di laurea, volti alla promozione delle competenze trasversali:

Modules not yet included

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Gestione carriere


Graduation

List of theses and work experience proposals

theses proposals Research area
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.