Due to the COVID-19 events, we virtually meet on Fridays from 1- 3:50 pm.
This course is about the role of data and data analytics in security and privacy. It covers applications of machine learning and big data analytics to various security and privacy problems, and also discusses the security and privacy problems associated with machine learning and big data. The topics include: the use of machine learning for making decisions related to security and privacy; misuse detection on social media; tracking technologies; data (de-) anonymization; anomaly detection; privacy-preserving machine learning algorithms, and adversarial machine learning.
Bachelor degree in Computer Science or equivalent. This course requires no prior experience in security and privacy, but assumes the willingness to seek out and read background material as needed. Although it is not a requirement, knowledge in core topics of data science and machine learning would be very helpful.
No required textbook. All course readings will be scientific papers. Each week the instructor lists these papers for students to read before the class.
This is a discussion-based graduate seminar course. You are expected to attend all class meetings and to participate actively in the discussion. Students need to inform the instructor in advance, if they cannot attend a class due to a conference or other major personal or professional obligation.
We will read and discuss a few papers every week. Every student must complete the assigned reading prior to the class so that they can participate fully in class discussions. To facilitate productive class discussions, students must submit a review of each of the assigned papers to Canvas on Fridays by 11:59 am. Reviews should consist of three brief paragraphs in students’ own words with the following structure:
Paragraph 1:
Explain the problem and motivation.
Explain the main ideas and technical contributions of the work.
Compare this work with the main prior work.
Explain the methodologies used for evaluation.
List three key strengths and three key weaknesses of this paper. Focus should be more on approach and evaluation.
Paragraph 3:List any future work that you might consider in this line of research.
Students will be the discussion leader for class discussions of two different papers (on two different days). As the discussion leader, the student must prepare a 30-minute presentation that covers the main technical contributions of the paper, plus at least 10 discussion questions. Prior to the beginning of class, the student should upload their slides as a PDF to the “students-slides” folder on the Canvas. The moderator must use those questions to lead the discussion. Other students must participate and contribute to the discussion.
Students will work on a major project in groups of size 2--3. Students should propose their own projects, however, if needed some projects will be suggested by the instructor. Here is the timeline for the projects:
Thursday, February 4th (11:59 pm): Every student submits at least two project ideas for their project. Explain each idea in one or two paragraphs: state your research questions, motivation and general approach.
Friday, February 5th : Every student has 10 minutes to present their ideas in the class. At the end of this class students should decide about their projects and their groups.
From Monday, February 8th, to Friday, February. 12th: The instructor schedules meetings with each group to discuss and refine their idea.
Thursday, February 25 (11:59 pm): Group submit a finalized version of their project proposal in 2-3 pages. The proposal should state their research questions; hypotheses (if any); general approach; and evaluation metrics. It should also include a timeline with checkpoints and deliverables at those checkpoints. Describe what you hope to accomplish by the end of the semester.
Every week in class: Each group gives an oral progress report (~5 minutes). Describe your progress, and discuss your plan for the next week.
Thursday, March 25th (11:59 pm) Thursday, April 1st (11:59 pm): Groups submit a written progress report. Their written progress report should describe their progress to date relative to their proposed timeline, note any problems they have run into, describe your updated plan for the rest of the semester, and include any preliminary results or technical accomplishments. This written report should also include a draft related work section for your final paper. This report should be written in the format of a paper using the templates provided here .
Friday, April 30th: Give a 25-minute final project presentation in class.
Monday, May 10th (11:59 pm): Submit a paper including an abstract, introduction (including research questions), related work, methodology, results, discussion, conclusions, and references.
Students are encouraged to submit their project as a full paper to a conference with an appropriate deadline. A paper submission will likely require additional work after the end of the semester.
Your final paper should be written in a style suitable for publication at a conference or workshop. Papers should be in double-column ACM format (see https://www.acm.org/publications/proceedings-template ).
| Week | Class Date | Papers | Moderator |
|---|---|---|---|
| 1 | Jan. 22 | Introduction and Planning
|
Shirin Nilizadeh |
| 2 | Jan. 29 | Phishing and Scam
|
Shirin Nilizadeh Nihal Kumarswamy |
| 3 | Feb. 5 | Project ideas
|
|
| 4 | Feb. 12 | Abuse characterizaation and detection on Social Media
|
Shivani Patwardhan Ana Aleksandric Krishna Chaitanya Naragam |
| 5 | Feb. 19 | Web
|
Anahita Samadi (Moved to March 12) Daniel Adami (Moved to Feb. 26) Aleksandar Aleksandric (Moved to April 9) |
| 6 | Feb. 26 | IoT
|
Nihal Kumarswamy Marco Paramo |
| 7 | March 5 | Online Reviews
|
Krishna Chaitanya Naragam Daniel Adami Satvik Sharma |
| 8 | March 12 | Software Security
| Aaron Koli Shivani Patwardhan |
| 9 | March 19 | Spring Break | |
| 10 | March 26 | Privacy
|
Ana Aleksandric Debapriya Banerjee Fadiah Qudah (Moved to April 2) |
| 11 | April 2 | Machine Learning Privacy
|
Marco Paramo Aakash Singh |
| 12 | April 9 | Machine Learning (Speech and Image Recognition)
|
Fadiah Qudah Satvik Sharma |
| 13 | April 16 | Machine Learning Attacks and Defenses
|
Aaron Koli Aakash Singh |
| 14 | April 23 | Machine Learning Attacks and Defenses (NLP)
|
Anahita Samadi Debapriya Banerjee Aleksandar Aleksandric |
| 15 | April 30 | Final project presentation | |
Course grades will be based on the following. This class will have no exams. Final project presentations will be held on the last day of class.
To accommodate for unavoidable circumstances, you get four automatic 1-day extensions for individual paper summaries without having to ask me for an extension. Just note the extension request on the summary. Finally, you can drop 3 summaries, but at most 1 for a given week. Save these for circumstances such as falling ill or interviewing.
Students will be penalized 25% for every day it is late beyond the designated deadline. You are required to submit satisfactory versions of these reports within 4 days of the deadline to pass the class. This policy will be used for each of the project proposals, progress report, and final report.
Students may drop or swap (adding and dropping a class concurrently) classes through self-service in MyMav from the beginning of the registration period through the late registration period. After the late registration period, students must see their academic advisor to drop a class or withdraw. Undeclared students must see an advisor in the University Advising Center. Drops can continue through a point two-thirds of the way through the term or session. It is the student's responsibility to officially withdraw if they do not plan to attend after registering. Students will not be automatically dropped for non-attendance. Repayment of certain types of financial aid administered through the University may be required as the result of dropping classes or withdrawing. For more information, contact the Office of Financial Aid and Scholarships (http://wweb.uta.edu/aao/fao/).
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I pledge, on my honor, to uphold UT Arlington’s tradition of academic integrity, a tradition that values hard work and honest effort in the pursuit of academic excellence.
I promise that I will submit only work that I personally create or contribute to group collaborations, and I will appropriately reference any work from other sources. I will follow the highest standards of integrity and uphold the spirit of the Honor Code.
UT Arlington faculty members may employ the Honor Code in their courses by having students acknowledge the honor code as part of an examination or requiring students to incorporate the honor code into any work submitted. Per UT System Regents’ Rule 50101, §2.2, suspected violations of university’s standards for academic integrity (including the Honor Code) will be referred to the Office of Student Conduct. Violators will be disciplined in accordance with University policy, which may result in the student’s suspension or expulsion from the University. Additional information is available at https://www.uta.edu/conduct/.