Due to the COVID-19 events, this course switched to online modality as of March 26, 2020. We virtually meet on Thursdays from 11 am- 1:50 pm. We continue using Canvas for sharing materials and submitting review assignments, and we start using Microsoft Teams to have presentations and discussions. A Team has already been created on Microsfot Teams, so you need to learn how to Install Teams, and how to Join a Teams meeting. Please send the TA an email, if you do not have a good Internet connection or a device to use Microsoft Teams.
As a result, the assignments from now to the end of the semester is adjusted to reflect this change. Please note the changes made in the assignments as indicated by highlighting. The instructor reserves the right to make additional changes to the assignments or grading schema as needed to complete this course on time, and respond to university policies. If you have any concerns or questions, please send an email to me or the TA.
A main part of this class is research projects. Please continue working on your projects while practicing social distancing. To facilitate your virtual communoications, we have created six sub-groups (Channels) under our course's Team site, one for each group project. Each channel is private and can only be accessed by the group members for that channel and the instructor. With these channels, you can send messages to your teammates, do video-chatting, and also share your screens.
The structure of presentations will remain the same. Moderators should prepare slides, and during our virtual class, they will present it on our Team site. Moderators will ask questions, and students should indicate their interest in responding by putting their names in the chatbox. Then, the moderators will call students' names to talk.
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 Wednesdays by 11:59 pm. 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:
Wednesday, February 5th (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.
Thursday, February 6th : 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 Friday, Feb. 7th, to Wednesday, Feb. 12th: The instructor schedules meetings with each group to discuss and refine their idea.
Friday, February 14th (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.
Wednesday, April 8th (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 .
Thursday, May 7th: Give a 25-minute final project presentation in class.
Monday, May 11th (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. 23 | Introduction and Planning
|
Shirin Nilizadeh |
| 2 | Jan. 30 | Phishing and Scam
|
Shirin Nilizadeh Mohit Singhal Aditya Pande (on Feb. 20) |
| 3 | Feb. 6 | Phishing and Scam
|
|
| 4 | Feb. 13 | Social Media
|
Juan Gonzalez German (David Jaime and Taarika Ratra) |
| 5 | Feb. 20 | Web
|
(Nikhal Kamichetty and Huadi Zhu) HariKiran Venkata |
| 6 | Feb. 27 | IoT
|
Mohit Mohit Srinivasan Murali |
| 7 | March 5 | Online Reviews
|
Henry Serubiri Kinene (HariKiran Venkata and Aditya Pande) Nikhal Kamichetty |
| 8 | March 12 | Spring Break | |
| 9 | March 19 Cancelled due to UTA's COVID-19 preparation. | Software Security
|
David Jaime (Presentation moves to April 2) Sumit Mokashi (Presentation moves to April 9) |
| 10 | March 26 | Privacy
|
(Srinivasan Murali and Mohit Mohit) Sadegh Moosavi |
| 11 | April 2 | Machine Learning Attacks (Speech and Image Recognition)
|
Sai Venkata Pavan Kumar Vinnakota Huadi Zhu |
| 12 | April 9 | Machine Learning Privacy
|
Anuraag Venkatapuram Sreenivas (Presentation moved to April 16) Abhimanyu Joshi |
| 13 | April 16 | Machine Learning Attacks and Defenses
|
(Sadegh Moosavi and Saksham Chawla) Saksham Chawla |
| 14 | April 23 | Machine Learning Attacks and Defenses
|
(Juan Gonzalez and Sumit Mokashi) (Sai Venkata Pavan Kumar Vinnakota and Anuraag Venkatapuram Sreenivas) |
| 15 | April 30 | Bias and Fairness
|
Tarrika Ratra (Mohit Singhal and Henry Serubiri Kinene) Abhimanyu Joshi |
| 16 | May 7 | 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 2 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.
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