Skip to main content
Unit outline_

OCSE5614: Data Privacy: Theory and Practice

Semester 2a, 2024 [Online] - Online Program

With unprecedented ability and opportunities to collect personal data or information, privacy has become an essential aspect of cybersecurity. Many industries have experienced intentional and unintentional privacy breaches in the last few years. As such, the general public is also becoming aware of the value of protecting their digital privacy, while regulatory authorities are introducing new privacy regulations and guidelines. This unit provides theoretical, design and practical skills that are required to design and deploy privacy preserving technologies to protect user privacy in collection, computation and management of data. Emphasis is placed on practical implications of privacy preserving technologies with examples and perspectives from industry.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
CSEC5614
Assumed knowledge
? 

CSEC5616 or OCSE5616 or ELEC5616 or INFO3616 or INFO2222

Available to study abroad and exchange students

No

Teaching staff

Coordinator Ali Anaissi, ali.anaissi@sydney.edu.au
Tutor(s) Aicha Chorana, aicha.chorana@sydney.edu.au
The census date for this unit availability is 16 August 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Supervised online based exam.
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Evaluation and Analysis of Privacy Regulations: GDPR, CCPA, Australian and British Privacy Act
Students are required to submit the assignment report on Canvas.
30% Week 05
Due date: 01 Sep 2024 at 23:59
N/A
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Assignment Research Paper draft on a Privacy Issue and Proposed Solution
Students will submit the drafted paper on the Canvas.
30% Week 06
Due date: 08 Sep 2024 at 23:59
N/A
Outcomes assessed: LO1 LO2 LO3 LO5

Assessment summary

Assessment 1: It is due in week 5, students will work individually on the topic "Evaluation and Analysis of Privacy Regulations: GDPR, CCPA, Australian and British Privacy Act", various use cases will be given to the students and they will map them on the specific regulation and give the analysis. More details will be provided on Canvas.

Assessment 2: In this assessment, students will work in the groups of 2 or 3. They will draft a conference level research paper, that will identify some existing privacy issue (through literature) and then propose a solution to handle it. More details will be provided on Canvas.

Final Exam –  Students must score at least 40% in the final exam to pass the unit (see Pass requirements).

Detailed information for each assessment can be found on Canvas.

Conditions for pass in this unit:

  • At least 40% in the final exam
  • At least 50% total

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average. 

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date: Deduction of 5% of the maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded. This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below: In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date: Deduction of 5% of the marks for each calendar day after the due date. After answers explained in the lab, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Data Privacy Independent study (2.5 hr) LO1 LO2 LO6
Introduction to Data Privacy Tutorial (1.5 hr) LO1 LO2 LO6
Week 02 Entropy and Differential Privacy Independent study (2.5 hr) LO1 LO2 LO3
Entropy and Differential Privacy Tutorial (1.5 hr) LO1 LO2 LO3
Week 03 Privacy Attacks, Privacy-Aware Machine Learning and Data Science Independent study (2.5 hr) LO1 LO3 LO5 LO6
Privacy Attacks, Privacy-Aware Machine Learning and Data Science Tutorial (1.5 hr) LO1 LO3 LO5 LO6
Week 04 Navigating the Legal Side of Privacy Regulations Independent study (2.5 hr) LO1 LO2
Navigating the Legal Side of Privacy Regulations Tutorial (1.5 hr) LO1 LO2
Week 05 Encrypted Computation and Passive Information Leakage Independent study (2.5 hr) LO1 LO4 LO5 LO6
Encrypted Computation and Passive Information Leakage Tutorial (1.5 hr) LO1 LO4 LO5
Week 06 Privacy and Practicality Considerations Independent study (2.5 hr) LO2 LO4 LO5 LO6
Privacy and Practicality Considerations Tutorial (1.5 hr) LO2 LO4 LO5 LO6

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand fundamental data privacy concepts including the importance in the digital age, K-Anonymity, anonymization, de-identification, pseudonymization, and understand their limitations and effectiveness in protecting individuals' privacy.
  • LO2. Identify and analyze key data privacy legislation: GDPR and its impact on organizations and individuals. And explore the role of data governance in ensuring privacy and compliance within organizations.
  • LO3. Evaluate risks to data privacy in case studies and real-world scenarios and develop risk assessment and mitigation strategies.
  • LO4. Apply privacy-by-design principles: Ensure privacy is considered from the outset in system, product, and service development.
  • LO5. Assess impact of emerging technologies on data privacy: AI, IoT, and explore associated challenges and solutions.
  • LO6. Analyze data protection frameworks: Privacy impact assessments, policies, and their role in safeguarding privacy rights and compliance.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

new unit. added learning outcomes, assessments and weekly activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.