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.
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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
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Primo semestre | Oct 3, 2022 | Jan 27, 2023 |
Secondo semestre | Mar 6, 2023 | Jun 16, 2023 |
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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
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.
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1° Year
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2° Year
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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.
Data Security & Privacy (2022/2023)
Teaching code
4S009066
Academic staff
Mila Dalla Preda, Mariano Ceccato, Federica Maria Francesca Paci
Coordinatore
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Secondo semestre dal Mar 6, 2023 al Jun 16, 2023.
Learning objectives
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.
Prerequisites and basic notions
none
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.
Bibliography
Didactic methods
Lectures, seminars, laboratory activities
Learning assessment procedures
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.
Evaluation criteria
The project aims to verify the ability to deepen (researching material in a critical way) and autonomously rework the concepts presented during the course.
Criteria for the composition of the final grade
The teacher evaluates the presentation and discussion of the project and proposes a mark out of thirty
Exam language
inglese
Type D and Type F activities
Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.
1. Insegnamenti impartiti presso l'Università di Verona
Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).
Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.
2. Attestato o equipollenza linguistica CLA
Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:
- Lingua inglese: vengono riconosciuti 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 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.
Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.
Modalità di inserimento a libretto: richiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it
3. Competenze trasversali
Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali
Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.
4. Periodo di stage/tirocinio
Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico): qui informazioni su come attivare lo stage.
Insegnamenti e altre attività che si possono inserire autonomamente a libretto
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.
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.
Graduation
Attachments
Title | Info File |
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387 KB, 27/04/22 |
List of theses and work experience proposals
theses proposals | Research area |
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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.