Neil Zhenqiang Gong

         Ph.D. Candidate
         Computer Science Division
         University of California Berkeley
         Office: 721 Soda Hall

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Inference Attacks to Big Data

Online social network (OSN) sites, blogging sites, and third-parties who can crawl these sites acquire a large amount of data from users. These data includes users' social links, attributes, and their writing texts. Due to privacy concerns, users might not provide their sensitive data to these data acquirers. For instance, OSN users might choose to not provide their sensitive attributes to OSN sites; OSN users might set some of their sensitive social links and attributes to be private using the privacy settings provided by OSN sites; and bloggers might hide their identity when they post sensitive texts. We study the inferences of these sensitive information using a large-scale dynamic Google+ social network dataset with 79 daily snapshots and a blog dataset with 100,000 blog authors. We demonstrate that these private information can be inferred via leveraging big data analytics techniques. Our results imply that it is important to acquire data in a privacy-preserving way.


Inferring users' identity via linguistic stylometry

Measuring and modeling the interactions between social links and user attributes, which shed light on the inferences of hidden social links and user attributes

Jointly inferring social links and user attributes


  • Dawn Song (Professor at UC Berkeley)
  • Arvind Narayanan (Professor at Princeton University)
  • Hristo Paskov (Ph.D. student at Stanford University)
  • Elaine Shi (Professor at University of Maryland)
  • Emil Stefanov (Ph.D. student at UC Berkeley)
  • Wenchang Xu (Visiting student from Tsinghua University)
  • Ling Huang (Research scientist at Intel Labs)
  • Prateek Mittal (Professor at Princeton University)
  • Vyas Sekar (Professor at CMU)
  • Ameet Talwalkar (Postdoc at UC Berkeley)
  • Lester Mackey (Professor at Stanford University)
  • John Bethencourt
  • Richard Shin (Ph.D. student at UC Berkeley)