Κατεύθυνση: Secondary Major in Networks or Telecommunications: Optional Courses

Students can choose it only once during undergraduate studies (either the 7th or the 8th semester).

Learning Outcomes

The purpose of the course is to highlight the necessity of a high-level framework, for the protection of personally identifiable information (PII) within information and communication technology (ICT) systems, general in nature, placing organizational, technical, and procedural aspects in an overall privacy framework. The privacy framework, according to ISO/IEC 29100:2011 and ISO/IEC 29101:2018, is intended to support organizations define their privacy safeguarding requirements related to PII within an ICT environment by defining the actors and their roles in processing PII, describing privacy safeguarding requirements, and referencing known privacy principles. According to ISO/IEC 2913:2017, guidelines are providing for conducting a Privacy Impact Assessment (PIA) as an instrument for assessing the potential impacts on privacy of a process, information system, program, software module, device, which processes PII. Privacy-by-Design critical issues are introduced. Moreover, privacy protection issues in cloud environments are described in detail, according to ISO/IEC 27018:2014.

In this context, the learning outcomes of the course, after its successful completion, are that the students will be able:

• to understand the basic concepts of privacy framework as well as how to recognize and analyze privacy requirements
• to realize the Privacy-By-Design principle
• to conduct a study for Privacy Impact Assessment
• to understand and deal with privacy protection issues in cloud environments.

Course Contents

• Privacy protection: Technical, legal, regulation, and ethical issues
• Privacy framework according to ISO/IEC 29100:2011 and ISO/IEC 29101:2018
• Privacy by Design critical issues
• Privacy Impact Assessment according to ISO/IEC 29134:2017
• Privacy protection countermeasures according to ISO/IEC 27701:2019
• Cloud computing and related Privacy protection issues according to ISO 27018:2014
• GDPR and ISO 27001 synergies of activities towards organization’s compliance
• Case study: Privacy in social media

Recommended Readings

• Lambrinoudakis, L. Mitrou, S. Gritzalis, S. Katsikas (2010), Privacy Protection and Information and Communication Technologies (Eds.), Papasotiriou Pubs. (in Greek)
• Acquisti, S. Gritzalis, C. Lambrinoudakis, S. De Capitani di Vimercati (Eds) (2008) Digital Privacy, Theory, Technology and Practices, Auerbach Publications.
• Tamo-Larrieux (2018), Designing for Privacy and its Legal Framework: Data Protection by Design and Default for the Internet of Things, Springer
• van der Sloot, A. de Groot, (2018) The Handbook of Privacy Studies, Amsterdam University Press
• ISO/IEC 29100:2011 Information Technology – Security Techniques – Privacy Framework
• ISO/IEC 29134:2017 Information Technology — Security Techniques — Guidelines for Privacy Impact Assessment
• ISO/IEC 27701:2019 Information Technology – Security Techniques – Extension to ISO 27002:2013 for Privacy Information Management – Requirements and Guidelines
• ISO/IEC 27018:2014 Information technology — Security techniques — Code of practice for protection of personally identifiable information (PII) in public clouds acting as PII processors

Learning Outcomes

Course Contents

• Introduction to Telemedicine
• Biomedical Data Coding and Compression
• Biomedical Data Processing for Telemedicine Applications
• Video Communication for Telemedicine Applications
• Telemedicine Networks
• Home Care Systems
• Context Aware Telemedicine Systems
• Wireless Telemedicine and Ambient Assisted Living
• Wearable Systems
• Clinical Applications of Telemedicine
• Security in Telemedicine systems
• Case Studies – Project Assignments

Recommended Readings

• Medical Informatics, e-Health: Fundamentals and Applications (Health Informatics) Softcover reprint of the original 1st ed. 2014 Edition by Alain Venot (Editor), Anita Burgun (Editor), Catherine Quantin (Editor)
• Telemedicine Handbook, Pompidou Alain, Apostolakis I, Α., Ferrer – Roca Olga, Sosa – Iudicissa Marcelo, Allaert Francois, Della Mea Vincenzo, Kastania Anastasia N.

Learning Outcomes

The objective of this course is to familiarize students with: (a) learning access methods for large data volumes for various data formats, as well as scalable writing, (b) efficient data storage and retrieval with appropriate indexing techniques, (c) the design and implementation of data processing algorithms aiming at the development of efficient applications that manage data.

Upon successful completion of the course, the students will be in position:

• to develop data-centric applications with emphasis in efficiency and scalability
• to use the most appropriate indexing methods for a given problem
• to evaluate and improve the parts of data processing algorithms that incur high computational load
• to apply the most suitable data processing techniques that match with data under analysis and for a given query workload
• to develop efficient data processing algorithms

Course Contents

• Operation of disk and main memory, serial and random access, cost and efficiency, data locality on disk and main-memory, direct and indirect access, main-memory data structures (arrays, priority queues, hashing)
• Data access techniques for structured, semi-structured and unstructured data: relational DBs, XML, RDF, text documents, web pages, web APIs, social networks.
• One-dimensional data and indexing, B-tree, variations (B+tree, B*tree), range queries, inverted indexes.
• Spatial data, spatial data types, spatial queries, approximation in representation, distance measures, extensions for multidimensional data.
• Spatial indexing techniques, grid file, spatial indexes (R-tree, QuadTree), space-filling curves (Hilbert, Z-order)
• Similarity search, k-nearest neighbor search, branch-and-bound algorithms, locality sensitive hashing (LSH), approximate k-nearest neighbor.
• Top-k search: algorithms based on pre-processing, online algorithms, Fagin’s algorithm, index-based algorithms.
• Join queries, spatial joins, top-k joins.
• Spatio-textual data, query types, indexing methods, processing algorithms.

Recommended Readings

• Ramakrishnan R. & Gehrke J. (2002): Database Management Systems (3rd Edition), McGraw Hill.
• N.Mamoulis (2011): Spatial Data Management, Synthesis Lectures on Data Management, Morgan & Claypool.

Learning Outcomes

Upon successful completion of the course the students will be able:

• to know and understand the key concepts of digital teaching and learning
• to analyse, assess, select and justify pedagogically appropriate e-learning methods and tools for digital teaching and learning innovations.
• to design and create pedagogically grounded online courses.

Course Contents

• Online Teaching and Learning: Theoretical Underpinnings
• Educational Design for Online Teaching and Learning
• An hierarchical Open Access to Online Education framework: Elements (Open Educational Resources, Learning Activities and Lesson Plans, Online Courses, Digital Learning Spaces). Tools and Key Roles (Online Education Instructional Designers, e-Tutors, e-Learning Systems Administrators, Managers)
• Open Educational Resources: Learning Objects, Educational Metadata, Repositories of Learning Objects. Case Studies: the National Repositories of Learning Objects
• Learning Activities and Lesson Plans: Authoring Tools for Learning Activities and Lesson Plans, Repositories of Learning Activities and Lesson Plans. Case Studies: the National Repositories of Learning Activities and Lesson Plans
• Design, Development and Delivery of Online Courses: Methodology for Designing Online Courses. Authoring Tools for Developing Online Courses. Course Management Systems. Case Study: Open edX, MOODLE
• Digital Learning Spaces: 3D Virtual Classrooms and Laboratories

Recommended Readings

• Textbook in Greek (provided for free)
• Additional Open Access Educational Resources available through the course management system