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Leakage

Leakage. Shiya Chen IMSE. Agenda. You Are What Y ou L ike! Information Leakage Through Users’ Interest Break I still know what you visited last summer. Introduction.

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Leakage

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  1. Leakage Shiya Chen IMSE

  2. Agenda • You Are What You Like! Information Leakage Through Users’ Interest • Break • I still know what you visited last summer

  3. Introduction In online social network(OSN), a user’s private data might be missing or hidden. But interest is highly available to public. However, by exploiting pubic attribute of other users who share similar interest, we can infer one’s private attribute. Most users who likes “Justin Bieber” and “My World 2.0” are female teenagers User X likes “EenieMeenie” X is a female teenager!

  4. Introduction 2 components Deriving semantic correlation between words to link users who share similar interest. Deriving statistics about these users by analyzing their public Facebook profile.

  5. Introduction Obstacles Many interests are ambiguous Drawing semantic link between different interests is difficult Interests are user generated, which can be quite different from marketers’ classified items. Main Goal To show how seemingly harmless information, if augmented with semantic knowledge, can leak private information

  6. Attacker Model

  7. From Interest Names to Attribute Inference Step 1 Creating Interest Description 1.1 Extract interest names from user profile 1.2 Augment interest with semantically related words mined from Wikipedia Why Wikipedia? Up-to-date Largest collection Multilingual

  8. From Interest Names to Attribute Inference Wikipedia(cont.) Fundamental Category Category Category Category Category Category Article Article Article Article Article Hyperlink(Semantically Related) Article

  9. From Interest Names to Attribute Inference How to describe interest via Wikipedia? Find the collection of the parent categories of its most related Wikipedia articles.

  10. From Interest Names to Attribute Inference Step 2 : Extracting Semantic Correlation LDA (Latent DirichletAllocatio): Capture statistical properties of text document in term of underlying topics ….N documents.. Cluster of highly co-occurred words Topic 1 Topic 2 Topic 3 “American blue singer” “American soul singer” “Arab musician”

  11. From Interest Names to Attribute Inference Step 3 Interest Feature Vector (IFV) Extraction The probability that a user is interested in topic i is the probability that his interest description belongs to topic i.

  12. From Interest Names to Attribute Inference Step 4 Inference 4.1 Neighbors Computation Define an appropriate distance between users by IFV 4.2 Inference Infer a user’s hidden profile attribute x from that of its l nearest neighbors First, select the l nearest neighbor whose attribute x is defined and public. Then do majority voting for the hidden value.

  13. Experiment and Result 2 Data Set: Raw data and volunteer data

  14. Q & A

  15. Agenda • You Are What You Like! Information Leakage Through Users’ Interest • Break • I still know what you visited last summer

  16. Agenda • You Are What You Like! Information Leakage Through Users’ Interest • Break • I still know what you visited last summer

  17. Outline What is history sniffing attack and its background 3 types of automated sniffing attack and how they’re defended against Interactive attacks and its experiment Side channel attack and its experiment Conclusion

  18. Introduction History Sniffing Attack: Allow web sites to learn about users’ visits to other sites. It’s enabled by CSS (Cascading Style Sheet) and JavaScript. “Same Origin Policy” Thread Model: Can not simply get a list of URLs victims have visited. Learn which of the predetermined website is visited. Can not eavesdrop on, tamper with or redirect network traffic from victims to legitimate website. Attack Consequences: Benign or beneficial: Online banking protection, advertising Harmful: Privacy leakage, more targeted phishing sites.

  19. Automated Attacks Direct sniffing. A JavaScript program can use Document Object Model (DOM),a standardized API, to examine the page it’s embedded within. DOM provides access to the computed style of each HTML element. e.g. a { text-decoration: none } a:link { color: #A61728 } a:visited { color: #707070 }

  20. Automated Attacks 2. Indirect Attack 2.1 Make visited and unvisited links take different amounts of space. Inspect positions unrelated elements. 2.2 Make visited and unvisited links cause different images to load.

  21. Automated Attacks 3. Side-channel sniffing A system leaks information through a mechanism that wasn’t intended to provide that information. Timing attack: A cache returns a piece of information faster than it could be retrieved from the source. An attacker can makes it takes longer to draw a page if a link is visited, by making color partially transparent or underlining a line of text JavaScript has access to system clock and force page layout to occur synchronously.

  22. Automated Attacks 4. Defense Limit the ability of CSS to control the visited/unvisited distinction. Same size Same time to draw Only change color Cannot remove or introduce gradient Defense against timing attack Make sure that selector matching takes the same amount of time whether a link is visited. Only one history lookup per style rule and will do it last

  23. Interactive Attacks Experiment 1 Interactive attack requires victims to interact with them, where victims’ actions on a site reveal their browsing history. CAPTCHA Word CAPTCHA:

  24. Interactive Attacks Experiment 1 2. Character CAPTCHA An 8 character CAPTCHA can probe 24 sites. A 12-character can probe 36.

  25. Interactive Attacks Experiment 1 3. Chessboard puzzle: A 10*10 chessboard can probe 100 sites.

  26. Interactive Attacks Experiment 1 4. Pattern Matching Puzzle:

  27. Interactive Attacks Experiment 1 Experiment goal: To prove these task could be performed by a typical user accurately, quickly and without frustration. 307 participants for “user study” Experiments divided into several tasks. Result: Usable data from 177 user

  28. Interactive Attacks Experiment 1 2 Factors: 1. How fast a victim can do it 2. How many URLs are encoded.

  29. Interactive Attacks Experiment 2 Webcam Attack: The color of an area of the screen depend on whether a link was visited The color illuminates the user and his environment 2 obstacle: Permission to activate the camera To probe many links, it is necessary to change the color frequently

  30. Interactive Attacks Experiment 2 Experiment: Make a rectangular box of uniform color be a hyperlink, periodically change it target, and monitored changes in the average color detected by camera. 20 links: 10 visited and 10 unvisited 2 Variant for experiment: Variant 1: comply with WCAG standard for seizure safety. Limited area to blink, limited blink rate, limited luminosity difference between flashes, avoid red. Variant 2:made the entire screen to flash and used brighter color

  31. Interactive Attacks Experiment 2 Result: 100% accuracy in bright room 50% accuracy in dark room 60 out of 307 perform it

  32. Conclusion Balancing act of browsers An unintended attack: History sniffing 3 automated attack 2 interactive attack.

  33. Thank You ! Q & A

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