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.
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 enrollment year.
1° Year
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2° Year activated in the A.Y. 2022/2023
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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)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)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)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)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 (2021/2022)
Teaching code
4S009066
Academic staff
Mila Dalla Preda, Mariano Ceccato, Federica Maria Francesca Paci
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
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.
Bibliography
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.
