COMP SCI 7213OL - Applied Privacy

Online - Online Teaching 6 - 2023

This course is to introduce students to understand privacy properties, apply privacy practices, and ultimately bring them to up to the frontier of modern privacy techniques that enterprises and governments have adopted. The course tries to prepare students to understand what the risks to human related privacy are and to rethink how to design and build products and systems with privacy and respect for their target users across countries in which they may operate. The course will also introduce the concept of pure provable privacy - differential privacy - and show how it is being put into practice. Differential privacy was initially incepted at Microsoft Research, and the theoretical and technical impact of differential privacy is profound in global industries, which has already been deployed by Google, Apple, Uber, and US Census Bureau. Finally, the course will show ways of achieving differentially private mechanisms through a practical translation and explore the limits of the use of differentially private techniques in synthetic data generation by demonstrating practical attacks that thwart privacy properties.

  • General Course Information
    Course Details
    Course Code COMP SCI 7213OL
    Course Applied Privacy
    Coordinating Unit Computer Science
    Term Online Teaching 6
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange
    Prerequisites Carousel 2 Courses: COMP SCI 7308OL and COMMGMT 7025OL
    Restrictions GDip.Cyber Security(OL) M.Cyber Security(OL)
    Course Staff

    Course Coordinator: Associate Professor Hung Nguyen

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    Upon completion of this course, students will be able to:
    1. Evaluate the compliance of privacy properties and policies of web and mobile applications with respect to Australian and International privacy laws and regulations.
    2. Interpret information privacy and data protection requirements to rethink the design and build of products to reduce risks in different privacy critical scenarios.
    3. Assess privacy vulnerabilities and design flaws of various products, such as web and mobile applications, to inform better designs in the future.
    4. Analyse privacy-preserving approaches in software systems to identify strengths and weaknesses of privacy-enhancing technologies including differential privacy.
    5. Develop appropriate differential privacy mechanisms when synthesising data tailored for real-world applications.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    CLO 1,2,3

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    CLO 1,4,5

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    CLO 2,5

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    CLO 1,3,4,5

    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    CLO 1,3,4

    Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency

    Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.

    CLO 1

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    CLO 4,5

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

    CLO 2
  • Learning Resources
    Required Resources

    Students need to have a development environment for Python for course assignments and other tasks.
  • Learning & Teaching Activities
    Learning & Teaching Modes

    Online with interactive tutorials/workshops

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The estimated total number of hours is 150 hours (over 6 weeks)
    Learning Activities Summary

    Week 1. Privacy and online rights
    Week 2. Data protection
    Week 3. Vulnerabilities and patch failures in data privacy
    Week 4. Introduction to differential privacy
    Week 5. Deployment of differential privacy
    Week 6. Privacy at a systems level and the future of privacy enhancing technologies
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    Assessment Name Due Weighting Related Weeks
    Privacy Assessment on Web and Mobile Apps End of Week 2 20% Weeks 1&2
    Attempts at Data Privacy End of Week 4 40% Weeks 3&4
    Differential Privacy Implementation End of Week 6 40% Weeks 5&6
    Assessment Detail
    Assessment 1
    Privacy Assessment on Web and Mobile Apps – Short Answer Questions
    Due end of Week 2
    Weighting: 20%

    In this assessment, students will review and interpret the requirements of privacy regulations and evaluate how they are applied to the privacy policies of selected web and mobile applications. Students will select two applications from a pool of provided web and mobile applications and respond to short answer questions on:
    • Privacy compliance in the privacy policies and data protection requirements of the selected applications
    • Potential privacy risks against privacy regulations and/or policies in the behavior of the selected applications
    • How to improve the design of the selected applications to reduce the privacy issues existing in their privacy policies.

    Assessment 2
    Attempts at Data Privacy – Proposal and code
    Due end of Week 4
    Weighting: 40%

    In this assessment, students will analyse the application of privacy-enhancing approaches in a given scenario and propose the implementation of a suitable approach to improve the protection of privacy. Students will be provided with a large dataset containing over 32,000 samples, from which they will generate several smaller, random datasets using a data generator program. The data generator program ensures that all the students will be working with different datasets. Students will be required to:
    • Identify and assess the privacy vulnerabilities and design flaws in the given scenario
    • Compare the strengths and weaknesses of different privacy-enhancing approaches and propose one to implement in the given scenario
    • Fill in missing Python code in provided code snippets using their proposed privacy-enhancing approach
    • Test their recommended privacy-enhancement approach by inputting publicly available datasets into the completed Python program and report on the results
    • Analyse whether the applied privacy approach improves the protection of data.

    Assessment 3
    Differential Privacy Implementation – Report and Code
    Due end of Week 6
    Weighting: 40%

    In Assessment 2, students explored a variety of privacy-enhancing approaches and proposed one to implement to improve the protection of data privacy. In this assessment, students will focus on differential privacy because it is a data sharing method that only shares some statistical characteristics without disclosing the information specific to the individual. In this assessment, students will adopt and implement differential privacy mechanisms to the scenario and dataset from Assessment 2 and submit a report detailing the performance and outcome of the implementation. Students will be required to:
    • Compare the strengths and weaknesses of applying differential privacy against the privacy-enhancing approach from Assessment 2 in the scenario
    • Develop and implement appropriate differential privacy mechanisms
    • Fill in missing Python code in provided code snippets using differential privacy mechanisms
    • Input the same publicly available dataset used in Assessment 2 into the completed Python program and report on the results
    • Analyse whether the implementation of differential privacy mechanisms improves the protection of data.

    Students will submit the assessment works as PDF documents and code via the link provided on the course assignment pages. 
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support

    Counselling for Fully Online Postgraduate Students

    Fully online students can access counselling services here:

    Phone: 1800 512 155 (24/7) 

    SMS service: 0439 449 876 (24/7) 


    Go to the Study Smart Hub to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)

  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.