Introduction to privacy and privacy properties, privacy-enhancing technologies for data anonymization, crypto-based solutions, machine learning security and privacy, location privacy, privacy of healthcare and genomic data, privacy in e-cash systems and blockchain, privacy in e-voting systems.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
Course Learning Outcomes: Course Learning Outcome Assessment
GenAI Policy: We follow the Generative AI policy guideline of Bilkent University which can be found here: https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
Introduction to Privacy I Introduction to Privacy II Privacy-Enhancing Technologies (PETs) for data anonymization Crypto-Based Solutions Machine Learning Security and Privacy - I Machine Learning Security and Privacy - II Midterm Presentations Privacy of Healthcare and Genomic Data Privacy in E-cash, Blockchains Privacy in E-cash, Blockchains Privacy in E-voting Location Privacy Final Presentations Final Presentations ECTS - Workload Table: Activities Number Hours Workload Course hours 14 3 42 Presentation (including preparation) 1 15 15 Preperation for the final exam 1 10 10 Individual or group work 14 3 42 Final exam 1 2 2 Project (including preparation) 1 40 40 Total Workload: 151 Total Workload / 30: 151 / 30 5.03 ECTS Credits of the Course: 5 Type of Course: Lecture - Project Course Material: Slides Teaching Methods: Lecturing