Privacy and Security Concerns in Social Networks in the Digital Age

Dima Kagan

Me and MY Research

  • Make a positive impact
  • Can make someone's life better
  • Has the potential to change policies
  • Can inspire more research
  • Can change regulations
  • Practical!

If what you are working on is not important, and it is not likely to lead to important things, why are you working on it?

― Richard Hamming

 

Complex Networks

400M

930M

3B

3M

420M

300K

Harassment and cyberbullying

Fake News

Malware

Spam

Bots & fake profiles

identity theft

Child Molesters

Labeling Data is Hard

Malicious Users Tend to Connect to Other Profiles Randomly

Link Prediction

Link Prediction

+

Crowd Wisdom

Topology Based

Feature Extraction

16 feautres

for directed

graphs

8 feautres for

undirected

graphs

◦ For undirected graphs:

  • Common Friends
  • Total Friends 
  • Jaccard’s-Coefficent 

 

     

    \frac{|\Gamma(v) \cap \Gamma(u)|}{|\Gamma(v) \cup \Gamma(u)|}
    |\Gamma(v) \cup \Gamma(u)|
    |\Gamma(v) \cap \Gamma(u)|
    |\Gamma(v)_{in}| \cap |\Gamma_{out}(u)|
    \begin{cases} 1, & \text{if}\ (u,v)\in E \\ 0, & \text{otherwise} \end{cases}

      ◦ For directed graphs:

    • Transitive Friends
    • Opposite Direction Friends

    Meta Feature Exteraction

    AbnormalityVertexProbability(v) := \frac{1}{|\Gamma(v)|}\sum\nolimits_{u \in \Gamma(v)}p(v,u)

    We extracted 7 features​

    •                 - the confidence that an edge is fake.  
    •  
    p(v,u)

    outline

    image/svg+xml 1 2 3 4 5 6 7 9 8 10 11 0.94
    image/svg+xml 1 2 3 4 5 6 7 9 8 10 11 0.94 0.84 0.56 0.12 0.22 0.44 0.16 0.32 0.91 0.72 0.59 0.14 0.23
    image/svg+xml 1 2 3 4 5 6 7 9 8 10 11 0.91 0.84 0.76 0.56 0.36 0.16 0.34 0.12 0.32 0.44 0.31
    image/svg+xml 1 2 3 4 5 6 7 9 8 10 11 0.91 0.84 0.76 0.56 0.36 0.16 0.34 0.12 0.32 0.44 0.31

    Kagan, Dima, Yuval Elovichi, and Michael Fire. "Generic anomalous vertices detection utilizing a link prediction algorithm." Social Network Analysis and Mining 8 (2018): 1-13.

    Datasets

    Network Is Directed Vertices Number Links Number Date Labeled
    Academia Yes 200,169 1,389,063 2011 No
    Anybeat Yes 12,645 67,053 2011 No
    ArXiv HEP-PH No 34,546 421,578 2003 No
    CLASS OF 1880/81 Yes 53 179 1881 Yes
    DBLP No 1,665,850 13,504,952 2016 No
    Google+ Yes 107,614 13,673,453 2012 No
    Orkut No 3,072,441 117,185,083 2012 No
    Twitter Yes 5,384,160 16,011,443 2012 Yes
    Xing No 1,053,754 2,161,968 2012 No
    Yelp No 249,443 3,563,818 2016 No

    Fully Simulated  Networks

    AUC TPR FPR Precision
    Simulation 1 (Arxiv HEP-PH) 0.991 0.889 0.011 0.904
    Simulation 2 (DBLP) 0.997 0.994 0.064 0.993
    Simulation 3 (Yelp) 0.993 0.917 0.007 0.937

    Semi Simulated Networks

    AUC TPR FPR Precision
    Academia 0.999 0.998 0.000 0.997
    Anybeat 1.000 0.996 0.001 0.996
    Arxiv HEP-PH 0.997 0.953 0.004 0.965
    DBLP 0.997 0.940 0.005 0.995
    Flixster 0.992 0.990 0.092 0.990
    Google+ 1.000 0.999 0.000 0.999
    Xing 0.999 0.955 0.005 0.951
    Yelp 0.996 0.941 0.005 0.958

    Real World Networks

    Kids Friendship Network

    Method AUC TPR FPR Precision
    Ours 0.91 0.89 0.15 0.964
    Strangers 0.71 0.44 0.006 1

    Twitter

    Information gain

    What About Communities?

    Anomalous Communities detection 

    Shay Lapid, Dima Kagan, and Michael Fire. “Co-Membership-based Generic Anomalous Communities Detection”. In: Neural Processing Letters (2022).

    Anomalous Communities detection 

    Shay Lapid, Dima Kagan, and Michael Fire. “Co-Membership-based Generic Anomalous Communities Detection”. In: Neural Processing Letters (2022).

    Work in progress 

    • Signed weighted networks
       
    • Case Study:  Unsupervised disease gene detection in protein-protein interaction (PPI) network

    covid-19 changed everything

    • 10.5% increase in active social media users.
    • Instagram reported a 70% increase in viewers of live videos from February to March when lockdown measures began.
    • TikTok user base increased the most, at 38%.

    Zooming Into Video Conferencing Privacy

    Video conferencing Privacy & Security

    Information Leakage

     

     

    Malware Attacks

     

     

    Data Breach

     

    Fake Avatars

     

     

    Phishing Attacks

     

     

    Zoombombing

    Multiparty Privacy

    Credit: Such, Jose M., and Natalia Criado. "Multiparty privacy in social media." Communications of the ACM 61.8 (2018): 74-81.

    89,305 Zoom related tweets

    90,395 Zoom related Instagram posts

    16,133 video conferncing images 

    26,408 images from Twitter

    78,435 images from Instagram

    A video conferencing detection model was trained based on ResNet-50.

    Accuracy = 0.969, TPR= 0.935, FPR =  0.016

    Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).

    89,305 Zoom related tweets

    90,395 Zoom related Instagram posts

    16,133 video conferncing images

    26,408 images from Twitter

    78,435 images from Instagram

    A video Conferencing detection model was trained based on ResNet-50.

    Accuracy = 0.969, TPR= 0.935, FPR =  0.016

    Duplicates removed using dhash

    Duplicates removed using the classifier last layer

    15,709 unique images

    Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).

    1. Face Detection - MTCNN + Azure Face API
    2. Face Embedding - dlib 128 dim vector
    3. Age detection = (Resnet-50 based model + Azure Face API)/2
    4. Gender Detection - Azure Face API
    5. Username Recognition - EAST  for detecting text location and MORAN  for subsequently recognizing the composing characters.
      Lemmatization was used to filter dictionary words that are not names.
      Separate words were merged into names based on their distance.
    6. Background detection - An Faster RCNN was trained to detect each participant screen

    Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).

    • A social graph was created by using face embedding.
    • Each user was connected to all other participants in the same meeting and to all his other meetings.
    • Also, a linkage can be done by the name and similar background.

    Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).

    Kagan, Dima, Galit Fuhrmann Alpert, and Michael Fire. "Zooming Into Video Conferencing Privacy." IEEE Transactions on Computational Social Systems (2023).

    What could possibly happen in zoom meeting

    What could possibly happen in zoom meeting

    The dataset

    • 30 videos
    • Total length of more than 8 hours
    • 65 abnormal events with various time windows.
    • The number of participants in the dataset ranges from 4 to 25.

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    😃

    🙂

    🙂

    🙂

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    Single Parcticipent Change Detection

    🙂

    🙂

    🙂

    🙁

    🙁

    🙁

    🙂

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Face

    Detection

    Face

    Tracking

    Feature

    Extraction

    Change

    Detection

    Group

    Change

    Detection

    Feature Extaction Change Detection Recall Precision TNR FPR
    Embedding 128-dim Arima
    Statistical Profiling    		 50.1%
    42.9%    		 83.2%
    75.0%    		 98.1%
    97.8%    		 0.9%
    1.4%
    Expressions 7-dim Arima
    Statistical Profiling    		 71.6%
    72.5%     		90.5%
    92.3%     		99.2%
    99.1%    		 0.5%
    0.8%
    Deep-face expressions Majority 67.1% 84.8% 97.7% 2.6%
    Method Results

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire Interruptions detection in video conferences  2023

    Social Network Research Challenges in 2023

    GenerativE AI

    BLIP-2

     

    VoiceBox

     

    STABLE DIFFUSION

     

    starcoder

    Social networks are changing

     

    • Twitter API -  $42k a month
    • Facebook - There is no API for data collection
    • LinkedIn - There is no API for data collection
    • NY Times - No more comments data for research for free.
    • Stack Overflow - ?
    • Reddit - $12,000 for 50 million API requests

    reddit protest

    Now what?!

    • pushshift.io - Reddit historical data
    • 4chan
    • Write crawlers - Is it legal?
    • Tiktok - Research API
    • GitHub
    • Telegram
    • YouTube
    • Unofficial APIs
    • Buy data from third-party companies

    Thank you questions?

    https://github.com/kagandi/

    kagandi@bgu.ac.il

    SOCIAL NETWORKS PRIVACY & SECURITY TAU

    By Dima Kagan

    Made with Slides.com

    SOCIAL NETWORKS PRIVACY & SECURITY TAU

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