• Title: Special Topics on Mobile Security
• Provided by: Dept. of Computer Engineering, Myongji University
• Lead by: Minho Shin (mhshin@mju.ac.kr, Rm5736)
• Period: Spring semester, 2020
• Location: 5734 at 5th Engineering Building
• Time: Mondays, 1pm to 4pm
• Type: Graduate Seminar
• Goal of the class
  • This class aims to familiarize students with current research topics in Mobile Security & Privacy area
    • This year, we focus on Privacy with AI
  • This class also aims to train students with their communication skills including oral presentation, discussion, writing, and collaboration
• Resources for researchers from Publishing Campus of Elsevier
  • Learning Center
  • Setting sail on your research journey

• Rules for the class
  • We have 15 presentations in total by three students
  • Each present 5 presentations throughout the semester
  • One presentation per day
  • The presenter announces the paper to present at least one week ahead
  • The presenter prepares a powerpoint slides for 30-60min talk
  • The other students submit a review article (1-2 pages) before class
  • The presentation should contain:
    • (Motivation) What are the motivations for this particular problem? What is the backgrounds for understanding the problem? Why is this important?
    • (Problem) What is, on earth, the exact problem the authors aim to address, and why on earth, is the problem important?
    • (Related work) What has been done by other researchers to address the same or similar problem on the table? Why the existing work is not enough to call done?
    • (Method) What is their main methodology to address the problem? How did they actually solve the problem in detail?
    • (Evaluation) What are the evidences for their success found in the paper? What is missing in their evaluation?
    • (Contribution) What is the contribution of the paper and what is not their contributions? Are there any limitations in their result? How would you evaluate the value of the paper?
    • (Future work) What is the remaining problems that were only partially addressed or never covered by the paper? What will be a possible approach to the problem?
  • A review article contains
    • The same content as described for the presenter
    • But in a succinctly written words form
    • Not exceeding two pages
    • Submit in Word/PDF by email
  • Evaluation
    • As a Presenter (10 points each)
      • Slide Quality
      • Talk Quality
      • Knowledge Level
    • As a reviewer (5 points each)
      • Clarity of the review
      • Understanding level

List: privacy-preserving_ai-2020.4.1.pdf

SAMPLE:

• (Taken by Alfarooq)Propagation- and Mobility-Aware D2D Social Content Replication
• (Taken by Henock)Understanding Smartphone Sensor and App Data for Enhancing the Security of Secret Questions
• SBVLC: Secure Barcode-Based Visible Light Communication for Smartphones

• (Taken by zz) Perfectly Privacy-Preserving AI

Privacy-preserving for AI applications:

1. Privacy Preserving in Blockchain based on Partial Homomorphic Encryption System for AI Applications
2. (Taken by zz) A Survey on Collaborative Deep Learning and Privacy-Preserving
   • (Taken by zz)Deep Models Under the GAN Information Leakage from Collaborative Deep Learning
3. Membership inference attacks against machine learning models
4. (Taken by Jafar)Privacy-Preserving Deep Learning
5. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
6. SecureML: A System for Scalable Privacy-Preserving Machine Learning
7. (Taken by Ejigu)Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

Privacy-preserving approach using machine learning:

1. (Taken by Jafar)A hybrid deep learning architecture for privacy-preserving mobile analytics
2. A machine-learning based approach to privacy-aware information-sharing in mobile social networks
3. The feasibility of dynamically granted permissions: Aligning mobile privacy with user preferences
4. Turtle Guard: Helping Android Users Apply Contextual Privacy Preferences
5. SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices
6. ConXsense – Automated Context Classification for Context-Aware Access Control
7. (Taken by zz)FlowIntent: Detecting Privacy Leakage from User Intention to Network Traffic Mapping
8. (Taken by Ejigu)AutoPer: Automatic Recommender for Runtime-Permission in Android Applications
9. Contextualizing Privacy Decisionsfor Better Prediction (and Protection)
10. Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection
11. iprivacy: Image privacy protection by identifying sensitive objects via deep multi-task learning

Others:

1. Crowd-ml: A privacy-preserving learning framework for a crowd of smart devices
2. Blind Data Classification Using Hyper-Dimensional Convex Polytopes
3. Deep learning with differential privacy