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Unit outline_

CSEC5614: Data Privacy: Theory and Practice

Semester 2, 2023 [Normal evening] - Camperdown/Darlington, Sydney

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
? 
OCSE5614
Assumed knowledge
? 

CSEC5616 or OCSE5616 or ELEC5616 or INFO3616 or INFO2222

Available to study abroad and exchange students

No

Teaching staff

Coordinator Muhammad Sajjad Akbar, muhammad.akbar@sydney.edu.au
Lecturer(s) Muhammad Sajjad Akbar, muhammad.akbar@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
hurdle task
Final Exam
Supervised paper based exam.
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
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 07
Due date: 16 Sep 2023 at 23:59
N/A
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO9
Assignment group assignment Research Paper draft on a Privacy Issue and Proposed Solution
Students will submit the drafted paper on the Canvas.
30% Week 12
Due date: 21 Oct 2023 at 23:59
N/A
Outcomes assessed: LO1 LO2 LO3 LO5 LO7 LO9 LO10 LO12
hurdle task = hurdle task ?
group assignment = group assignment ?

Assessment summary

Assessment 1: It is due in week 7, 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

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

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

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 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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Data Privacy Lecture (2 hr) LO1 LO2 LO6 LO7
Week 02 Entropy and Differential Privacy Lecture (2 hr) LO1 LO8 LO9 LO10
Entropy and Differential Privacy Tutorial (1 hr) LO1 LO8 LO9 LO10
Week 03 Data Privacy Attacks Lecture (2 hr) LO1 LO3 LO6 LO8
Data Privacy Attacks Tutorial (1 hr) LO1 LO3 LO6 LO8
Week 04 Privacy Aware Machine Learning Approaches Lecture (2 hr) LO1 LO5 LO6 LO7
Privacy Aware Machine Learning Approaches Tutorial (1 hr) LO1 LO5 LO6 LO7
Week 05 Privacy Aware Machine Learning Approaches Lecture (2 hr) LO1 LO5 LO6 LO7
Privacy Aware Machine Learning Approaches Tutorial (1 hr) LO1 LO5 LO6 LO7
Week 06 Legal Aspects of Privacy: EU/USA/AU Lecture (2 hr) LO2 LO7 LO8 LO11
Legal Aspects of Privacy: EU/USA/AU Tutorial (1 hr) LO2 LO7 LO8 LO11
Week 07 Privacy and Practicality conditions Lecture (2 hr) LO4 LO6 LO11 LO12
Privacy and Practicality conditions Lecture (1 hr) LO4 LO6 LO11 LO12
Week 08 Encrypted Computation in Data Privacy Lecture (2 hr) LO1 LO4 LO5 LO7
Encrypted Computation in Data Privacy Tutorial (1 hr) LO1 LO4 LO5 LO7
Week 09 Passive Information Leakage and data Privacy Lecture (2 hr) LO1 LO2 LO6 LO9
Passive Information Leakage and data Privacy Tutorial (1 hr) LO1 LO2 LO6 LO9
Week 10 Passive Information Leakage and data Privacy Lecture (2 hr) LO1 LO2 LO6 LO9
Passive Information Leakage and data Privacy Tutorial (1 hr) LO1 LO2 LO6 LO9
Week 11 Challenges in Data Privacy Lecture (2 hr) LO3 LO6 LO11 LO12
Challenges in Data Privacy Tutorial (1 hr) LO3 LO6 LO11 LO12
Week 12 Industrial Guest Talk on Practical Data Privacy Lecture (2 hr) LO2 LO4 LO5 LO6
Privacy Implementation Tutorial (1 hr) LO2 LO4 LO5 LO6
Week 13 Review Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
Review the unit Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12

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.

Required readings

 

Practical Data Privacy    Katharine Jarmul        2023

Recommended Reading:
CIPM (Exam Guide)    Peter. H. Gregory        2021


Understanding Cybersecurity Law and Digital Privacy    Melissa Lukings, Arash Habibi Lashkari    Springer    Dec, 2021
 

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 the fundamental concepts and principles of data privacy, including the importance of privacy in the digital age, anonymization, K-Anonymity and the legal and ethical implications of data privacy violations.
  • LO2. Identify the key legislation and regulations related to data privacy, such as the General Data Protection Regulation (GDPR) and analyze their impact on organizations and individuals.
  • LO3. Evaluate the risks and threats to data privacy, including data breaches, unauthorized access, and data profiling, and develop strategies to mitigate these risks.
  • LO4. Apply privacy-by-design principles to the development of systems, products, and services to ensure privacy is considered from the outset.
  • LO5. Assess the impact of emerging technologies, such as artificial intelligence and Internet of Things (IoT) on data privacy and explore the associated challenges and potential solutions.
  • LO6. Analyze the role of data protection frameworks, privacy impact assessments, and privacy policies in safeguarding individuals' privacy rights and organizational compliance.
  • LO7. Examine the concepts of data anonymization, de-identification, and pseudonymization, and understand their limitations and effectiveness in protecting individuals' privacy.
  • LO8. Explore the role of data governance and data stewardship in ensuring privacy and compliance within organizations, including data classification, data retention, and data sharing practices.
  • LO9. Critically analyze case studies and real-world scenarios to apply data privacy principles and frameworks, develop risk assessment and mitigation strategies, and make informed decisions in complex privacy-related situations.
  • LO10. Explore the data privacy with Differential privacy concepts, measurement of information, entropy and discussion on the use cases.
  • LO11. Exploring various industrial and practical considerations for the privacy with uses cases including: Google, Facebook, twitter, IBM, Microsoft, and Apple etc.
  • LO12. Exploring privacy implementation in network streams like IoTs and 5G networks etc. by considering various use cases.

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.

This is the first time this unit has been offered

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.