Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Privacy-Preserving Technologies

    A tantárgy neve magyarul / Name of the subject in Hungarian: Személyes adatok védelme

    Last updated: 2019. április 3.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Electrical Engineering,
    Engineering Information Technology,

    Elective course
    Course ID Semester Assessment Credit Tantárgyfélév
    VIHIAV35   2/0/0/f 2  
    3. Course coordinator and department Dr. Buttyán Levente, Hálózati Rendszerek és Szolgáltatások Tanszék
    4. Instructors

    Dr. Gergely Ács assistant professor,Department of Networked Systems and Services

    Dr. Levente Buttyán associate professor, Department of Networked Systems and Services

    6. Pre-requisites
    The course cannot be taken for students who already took VIETM294 Személyes és közadatok kezelése
    7. Objectives, learning outcomes and obtained knowledge This course provides an introduction into the practical problems of data protection and privacy. Students can develop skills of understanding and assessing privacy threats and designing countermeasures. The course focuses on the problem of unwanted personal and sensitive data leakage from different information sources (e.g., large datasets, web-tracking, encrypted traffic, source/binary code, machine learning models), and its detection as well as mitigations using Privacy Enhancing Technologies (PETS).  The objective of the course is to provide skills needed by Data Protection Officers (DPO) and also required by the European General Data Protection Regulation (GDPR).
    8. Synopsis

    1. week
    Introduction and Motivation

    2. week
    Legal background of Data Protection: GDPR

    3. week
    Cryptography: Homomorphic Encryption, Private Set Intersection, Secure Multiparty Computation

    4. week
    Privacy-preserving communication 1: TOR and attacks on TOR

    5. week
    Privacy-preserving communication 2: Secure Messaging (Signal), Oblivious Transfer, Private Information Retrieval

    6. week
    Web Tracking and Anti-Tracking

    7. week
    Personal data leakage from relational data: Uniqueness, Attribute Inference, Linking

    8. week
    Personal data leakage from unstructured data: Detection with Machine Learning, Web page fingerprinting, Code stylometry

    9. week
    Personal data leakage from aggregate data: Query auditing, Location recovery from density, Membership attack

    10. week
    Data anonymization: K-anonymity, Differential Privacy, RAPPOR

    11. week
    Privacy in Machine Learning: Modell inversion, Membership attack, Fairness

    12. week
    Interdependent Privacy

    13-14. week
    Psychological profiling and manipulation, Cognitive Security

    9. Method of instruction Lectures
    10. Assessment

    a.    during the semester:
    Fulfilling the requirements 1 classroom test.
    The final grade is the grade obtained for the test.

    b.    during the exam period: -

    c.    preliminary exam: - 

    11. Recaps Failed classroom tests can be retaken again on the supplement week.
    12. Consultations Information given at the course's website
    13. References, textbooks and resources Course material (lecture notes) is available in electronic format. Each lecture has separate references
    14. Required learning hours and assignment



    Preparation for lectures


    Preparation for classroom test




    15. Syllabus prepared by Dr. Ács Gergely    assistant professor    BME-HIT,
    Dr. Levente Buttyán    associate professor    BME-HIT