This course is about the role of data and data analytics in security and privacy. It covers machine learning and big data analytics applications for various security and privacy problems. Also, it discusses the security and privacy problems associated with machine learning and big data. The topics include using 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 participate actively in the discussion. Students must inform the instructor if they cannot attend a class due to a conference or other major personal or professional obligation.
We will read and discuss 2 or 3 papers every week. Every student must complete the assigned reading before class to participate fully in class discussions. To facilitate productive class discussions, students must submit a review of each assigned paper 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 one paper’s class discussion. As the discussion leader, the student must prepare a 30-minute presentation covering the paper's main technical contributions, plus at least ten discussion questions. Before the beginning of class, the student should upload their slides as a PDF to the Canvas folder for that reading assignment.
Students will work on a major project in groups of sizes 2--3. Students should propose their projects. However, if needed, the instructor will suggest some projects. Here is the timeline for the projects:
Thursday, February 9th (11:59 pm): Every student submits a project idea for their project. Explain your idea in one or two paragraphs: state your research questions, motivation, and general approach.
Friday, February 10th : Students have 5 minutes to present their ideas in class. At the end of this class, students will decide about their projects and their groups.
From Monday, February 13th, to Friday, February. 17th: The Instructor and the TA schedule meetings with each group to discuss and refine their idea.
Thursday, February 23th (11:59 pm): Each group submits 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 30th (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 28th: Give a 15-minute final project presentation in class.
Monday, May 8th (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.
| Week | Class Date | Papers | Moderator |
|---|---|---|---|
| 1 | Jan. 22 | Introduction and Planning
|
Shirin Nilizadeh |
| 2 | Jan. 27 | Phishing and Scam
|
Shirin Nilizadeh Shubham Shah Poojitha Thota |
| 3 | Feb. 3 | Abuse characterizaation and detection on Social Media
|
Vamsi Krishna Gaddamadugu jaswanth vemulapalli Krishnateja Yadlapalli |
| 4 | Feb. 10 | Project ideas
|
|
| 5 | Feb. 17 | Good Practices in Data-driven Security Research
| Shraddha Sharashchandra Varekar Pratik Dhanraj Chavan |
| 6 | Feb. 24 | Web
|
Suhas Holla Karkada Chandrashekar Rahul Nagireddi Manohar Pola |
| 7 | March 3 | Online Toxicity
|
Akhil Gajulavarti Krishna Vamsi Naragam Batreddi pavan surya |
| 8 | March 10 | Software Security
| Aknith Reddy Mekala NagaSuryaShivani Inti Raman Parimi |
| 9 | March 17 | Spring Break | |
| 10 | March 24 | Privacy
|
Abbinav Hariharan Vamsi Krishna Bobbala Venkateswara Mani Sri Harsha Akondi |
| 11 | March 31 | Machine Learning Privacy and Fairness
|
Rishabh Prasad Thakur Bhavanam Manikanta Sowmyasree Battu |
| 12 | April 7 | Security and Privacy of Speech and Image Recognition
|
Chris Richardson Rahul Reddy Periketi Supritha Garlapati |
| 13 | April 14 | DeepFakes
|
Youngtak Cho Shreya Manishkumar Patel |
| 14 | April 21 | Machine Learning Attacks and Defenses (NLP)
|
Arpit Rameshbhai Gabani Anupama Rani Neerukonda |
| 15 | April 28 | 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 unavoidable circumstances, you get three automatic 1-day extensions for individual paper summaries without asking 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 must submit satisfactory versions of your project reports within 4 days of the deadline to pass the class. This policy will be used for the proposal, 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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