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Keynote at 4th International Symposium on Secuirty in Computing at Communications

Keynote at 4th International Symposium on Secuirty in Computing at Communications

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With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. Websites like Facebook, Google+, YouTube, Orkut, Twitter and Flickr have changed the way Internet is being used. There is a dire need to investigate, study and characterize privacy and security on online social media from various perspectives (computational, cultural, psychological). Real world scalable systems need to be built to detect and defend security and privacy issues on online social media. I will describe briefly some cool ongoing projects that we have: Twit-Digest, MultiOSN, Finding Nemo, OCEAN, Privacy in India, and Call Me MayBe. Many of our research work is made available for public use through tools or online services. Our work derives techniques from Data Mining, Text Mining, Statistics, Network Science, Public Policy, Complex networks, Human Computer Interaction, and Psychology. In particular, in this talk, I will focus on the following: (1) Twit-Digest is a tool to extract intelligence from Twitter which can be useful to security analysts. Twit-Digest is backed by award-winning research publications in international and national venues. (2) MultiOSN is a platform to analyze multiple OSM services to gain intelligence on a given topic / event of interest (2) OCEAN: Open source Collation of eGovernment data and Networks Here, we show how publicly available information on Government services can be used to profile citizens in India. This work obtained the Best Poster Award at Security and Privacy Symposium at IIT Kanpur, 2013 and it has gained a lot of traction in Indian media. (3) In Finding Nemo, given an identity in one online social media, we are interested in finding the digital foot print of the user in other social media services, this is also called digital identity stitching problem. This work is also backed by award-winning research publication. I will be more than happy to clarify, discuss, any of our work indetail, as required, after the talk.

With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. Websites like Facebook, Google+, YouTube, Orkut, Twitter and Flickr have changed the way Internet is being used. There is a dire need to investigate, study and characterize privacy and security on online social media from various perspectives (computational, cultural, psychological). Real world scalable systems need to be built to detect and defend security and privacy issues on online social media. I will describe briefly some cool ongoing projects that we have: Twit-Digest, MultiOSN, Finding Nemo, OCEAN, Privacy in India, and Call Me MayBe. Many of our research work is made available for public use through tools or online services. Our work derives techniques from Data Mining, Text Mining, Statistics, Network Science, Public Policy, Complex networks, Human Computer Interaction, and Psychology. In particular, in this talk, I will focus on the following: (1) Twit-Digest is a tool to extract intelligence from Twitter which can be useful to security analysts. Twit-Digest is backed by award-winning research publications in international and national venues. (2) MultiOSN is a platform to analyze multiple OSM services to gain intelligence on a given topic / event of interest (2) OCEAN: Open source Collation of eGovernment data and Networks Here, we show how publicly available information on Government services can be used to profile citizens in India. This work obtained the Best Poster Award at Security and Privacy Symposium at IIT Kanpur, 2013 and it has gained a lot of traction in Indian media. (3) In Finding Nemo, given an identity in one online social media, we are interested in finding the digital foot print of the user in other social media services, this is also called digital identity stitching problem. This work is also backed by award-winning research publication. I will be more than happy to clarify, discuss, any of our work indetail, as required, after the talk.

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Keynote at 4th International Symposium on Secuirty in Computing at Communications

  1. 1. Privacy  and  Security  in  Online   Social  Media   Keynote  @  SSCC’16   Sept  22,  2016 Ponnurangam  Kumaraguru  (“PK”) Associate  Professor ACM  Distinguished  Speaker fb/ponnurangam.kumaraguru,  @ponguru
  2. 2. Who  am  I?   – Associate  Professor,  IIIT-­‐Delhi     – Ph.D.  from  School  of  Computer  Science,     Carnegie  Mellon  University  (CMU)     – Research  interests   -Social  Computing,  Computational  Social  Science,   Complex  Networks  pertaining  to  Human  Behavior,   specifically  in  the  context  of  Security  &  Privacy – Co-­‐ordinate  and  manage  Precog,   precog.iiitd.edu.in – ACM  Distinguished  Speaker   2
  3. 3. https://www.youtube.com/channel/UCHWDvG Dh4QjWbV79bM2neSg 3
  4. 4. 4 What  we  dabble  with!  
  5. 5. Non-­‐trustworthy  Content FAKE 5 $ RUMORS
  6. 6. Methodology 6
  7. 7. Training  Data – 500  Tweets  per  event – Used  CrowdFlower 7 Event Tweets Users Boston  Marathon  Blasts  (2013) 7,888,374 3,677,531 Typhoon Haiyan /  Yolanda  (2013) 671,918 368,269 Cyclone  Phailin (2013) 76,136 34,776 Washington  Navy yard shootings (2013) 484,609 257,682 Polar  vortex cold wave (2014) 143,959 116,141 Oklahoma  Tornadoes (2013) 809,154 542,049 Total     10,074,150 4,996,448
  8. 8. Credibility  Modeling   8 Feature  set   Features (45)   Tweet  meta-­‐data   Number  of  seconds  since  the  tweet;  Source  of  tweet  (mobile  /  web/   etc);  Tweet  contains  geo-­‐coordinates Tweet  content  (simple)   Number  of  characters;  Number  of  words;  Number  of  URLs;  Number  of   hashtags;  Number  of  unique  characters;  Presence  of  stock  symbol;   Presence  of  happy  smiley;  Presence  of  sad  smiley;  Tweet  contains   `via';  Presence  of  colon  symbol Tweet  content  (linguistic)   Presence  of  swear  words;  Presence  of  negative  emotion  words;   Presence  of  positive  emotion  words;  Presence  of  pronouns;  Mention   of  self  words  in  tweet  (I;  my;  mine) Tweet  author   Number  of  followers;  friends;  time  since  the  user  if  on  Twitter;  etc. Tweet  network   Number  of  retweets;  Number  of  mentions;  Tweet  is  a  reply;  Tweet  is  a   retweet Tweet links   WOT  score  for  the  URL;  Ratio  of  likes  /  dislikes  for  a  YouTube  video
  9. 9. Implementation
  10. 10. Feedback  by  Users 10
  11. 11. v
  12. 12. Harvard  (1839)  – Harvard  – Harvard  – Harvard  – MIT  – Northwestern  – UIUC  – WUSL  – CMU  (2009)  – IIITD   (2015)         12
  13. 13. http://twitdigest.iiitd.edu.in/TweetCred/ 13
  14. 14. De-­‐duplicating  audience Social  audience    =  437,632  +  153,000  +  805,097  or  less?? 14
  15. 15. Challenges 15 ProfessionalOpinion Dating Heterogeneous  OSNs Personal Degree  of  Details Quality  and  descriptive  personal   And  professional  information Little  personal  information   Descriptive  opinions Attribute  Evolution Time Information  evolved  on  one  but   not  on  other {jainpari,  Bangalore} Registration  with  same   information  on  both  OSNs {paridhij,  New  Delhi}
  16. 16. Generic  Identity  Resolution 16 Extract   available  &   discriminative features Candidate   Identities IDENTITY  SEARCH IDENTITY  LINKING Pairwise   Comparisons
  17. 17. Heuristic  Identity  Search 17 cerc.iiitd.ac.in Profile Content Self-mention Network Syntactic and Image Search Linking If self-identified / returned by more than one search method No Yes Candidate Identities name, location, username mobile no, post, friends, followers Paridhi  Jain,  Ponnurangam Kumaraguru,  and  Anupam Joshi.  2013.  @I  seek  ‘fb.me’:  Identifying  Users  across  Multiple  Online  Social   Networks.  In  Proceedings  of  the  22nd  International  Conference  on  World  Wide  Web,  WWW  ’13  Companion.  ACM,  New  York,  NY,  USA,   1259-­‐ 1268.  DOI=http://dx.doi.org/10.1145/2487788.2488160    [Honorable  Mention  Award}  
  18. 18. Harvard  (1839)  – Harvard  – Harvard  – Harvard  – MIT  – Northwestern  – UIUC  – WUSL  – CMU  (2009)  – IIITD   (2016)         18
  19. 19. 19
  20. 20. 20 How  many  of  you  have  posted   mobile  numbers  on  Online  Social   Networks? How  many  of  you  have  seen   mobile  numbers  being  posted  on   Online  Social  Networks?
  21. 21. Sample  posts 21
  22. 22. Sample  posts 22
  23. 23. Sample  posts 23
  24. 24. Sample  posts 24
  25. 25. Data  statistics – Twitter:  12th  October  2012  – 20th  October  2013 – Facebook:  16th  November  2012  – 20th  April  2013 25 Numbers Category  +91 Category  0 Category  void Total Twitter Facebook Twitter Facebook Twitter Facebook Twitter Facebook Mobile   Numbers 885 2,191 14,909 8,873 25,566 25,294 41,360 36,358 User   profiles 1,074 2,663 17,913 9,028 31,149 25,406 49,817 36,588
  26. 26. 26
  27. 27. SocialCaller  App 27 https://play.google.com/store/apps/details?id=com.ayush.socialcaller&hl=en
  28. 28. 28 20  Interviews 4  FGDs 10,427  Surveys #privacyindia12  Methodology 18  months!
  29. 29. 0.08 7.73 0.14 7.10 1.10 6.88 0.28 8.14 1.96 8.58 0.52 0.21 0.74 3.19 9.38 0.35 0.03 0.04 0.25 2.94 11.29 0.02 8.57 0.05 9.39 9.53 0.21 0.17 9.390.48 0.02 0.03 0.01 0.08 Sample  
  30. 30. Demographics Gender  (N=  10,232) Male 67.57 Female 32.43 30 Age   (N=10,350) <18 1.54 18-­‐24 21.31 25-­‐29 32.20 30-­‐39 25.90 40-­‐49 14.09 50-­‐64 4.46 65+ 0.50 Age
  31. 31. Internet  &  Social  Media What  do  you  feel  about  privacy  of  your  personal   information  on  your  OSN?   31 Q42,  N  =  6,855 It  is  not  a  concern  at  all   Since  I  have  specified  my  privacy  settings,  my   data  is  secure  from  a  privacy  breach   Even  though,  I  have  specified  my  privacy   settings,  I  am  concerned  about  privacy  of  my   data   It  is  a  concern,  but  I  still  share  personal   information   It  is  a  concern;  hence  I  do  not  share  personal   data  on  OSN  
  32. 32. Internet  &  Social  Media What  do  you  feel  about  privacy  of  your  personal   information  on  your  OSN?   32 Q42,  N  =  6,855 It  is  not  a  concern  at  all   Since  I  have  specified  my  privacy  settings,  my   data  is  secure  from  a  privacy  breach   42.13   Even  though,  I  have  specified  my  privacy   settings,  I  am  concerned  about  privacy  of  my   data   It  is  a  concern,  but  I  still  share  personal   information   It  is  a  concern;  hence  I  do  not  share  personal   data  on  OSN  
  33. 33. Internet  &  Social  Media What  do  you  feel  about  privacy  of  your  personal   information  on  your  OSN?   33 Q42,  N  =  6,855 It  is  not  a  concern  at  all   19.30   Since  I  have  specified  my  privacy  settings,  my   data  is  secure  from  a  privacy  breach   42.13   Even  though,  I  have  specified  my  privacy   settings,  I  am  concerned  about  privacy  of  my   data   23.84   It  is  a  concern,  but  I  still  share  personal   information   8.02   It  is  a  concern;  hence  I  do  not  share  personal   data  on  OSN   6.71  
  34. 34. Internet  &  Social  Media If  you  receive  a  friendship  request  on  your  most   frequently  used  OSN,  which  of  the  following  people   will  you  add  as  friends?   34 Q43,  N  =  6,929 Person  of  opposite  gender People  from  my  hometown Person  with  nice  profile  picture Strangers  (people  you  do  not   know) Somebody,  whom  you  do  not   know  or  recognize  but  have   mutual  /  common  friends  with Anyone
  35. 35. Internet  &  Social  Media If  you  receive  a  friendship  request  on  your  most   frequently  used  OSN,  which  of  the  following  people   will  you  add  as  friends?   35 Q43,  N  =  6,929 Person  of  opposite  gender People  from  my  hometown Person  with  nice  profile  picture 10.12 Strangers  (people  you  do  not   know) Somebody,  whom  you  do  not   know  or  recognize  but  have   mutual  /  common  friends  with Anyone
  36. 36. Internet  &  Social  Media If  you  receive  a  friendship  request  on  your  most   frequently  used  OSN,  which  of  the  following  people   will  you  add  as  friends?   36 Q43,  N  =  6,929 Person  of  opposite  gender 27.39 People  from  my  hometown Person  with  nice  profile  picture 10.12 Strangers  (people  you  do  not   know) Somebody,  whom  you  do  not   know  or  recognize  but  have   mutual  /  common  friends  with Anyone 2.99
  37. 37. Internet  &  Social  Media If  you  receive  a  friendship  request  on  your  most   frequently  used  OSN,  which  of  the  following  people   will  you  add  as  friends?   37 Q43,  N  =  6,929 Person  of  opposite  gender 27.39 People  from  my  hometown 19.51 Person  with  nice  profile  picture 10.12 Strangers  (people  you  do  not   know) 4.99 Somebody,  whom  you  do  not   know  or  recognize  but  have   mutual  /  common  friends  with 8.31 Anyone 2.99
  38. 38. 38 http://precog.iiitd.edu.in/research/privacyindia/
  39. 39. Takeaways – Online  Social  Media  is  a  different  beast  in   terms  of  privacy,  identity,  and  credibility -Research  /  technologies  should  be  developed – Multiple  interesting  research,  engineering,   and  innovation  waiting  to  be  done – Interested  in  hosting  students  – B.Tech.,   M.Tech.,  Ph.D. 39
  40. 40. 40 https://www.facebook.com/PreCog.IIITD/

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