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en:projects:details:yart [2016/06/23 11:26] (current)
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 +/* This is the template for project details pages */
  
 +/* 
 +  The database entry:
 +  "​type"​ is one of the following: phd theses, phd semester, master thesis, master semester, bachelor semester
 +  "​state"​ is one of the following: available, taken, completed (please upgrade accordingly!!!!!!!!!!) ​
 +  "​by"​ should be filled as soon as the project is taken/​completed
 +  "​completed_dt"​ is the date when the project was completed (YYYY-MM-DD). ​
 +  "​output_media"​ is the link to the pdf of the project (wiki syntax)
 +  "​table"​ must be "​projects"​ => don't touch it!
 +*/
 +---- dataentry project ----
 +title : Differential Privacy for Social Networks
 +contactname:​ Lyudmila Yartseva
 +contactmail_mail:​ lyudmila.yartseva@epfl.ch
 +contacttel: 31343
 +contactroom:​ BC148
 +type : master semester
 +state : unavailable
 +created_dt :  2011-09-26
 +taken_dt : YYYY-MM-DD
 +completed_dt : YYYY-MM-DD
 +by : the full name of the student
 +output_media : en:​projects:​mahdi_thesis.pdf|Download Mahdi'​s Thesis
 +table : projects
 +======
 +template:​datatemplates:​project
 +----
 +
 +/* Description of the project */
 +For the purpose of government and scientific research of social networks, it is desirable to have possibilities to reveal the information about a Social Network'​s structure.
 +Applying a small amount of random modifications to the social network before publishing its structure or some aggregate characteristic is a common approach to preserving privacy. It is called anonymization.
 +In the realm of relational databases, the notion of differential privacy is used as a means to measure the amount of utility of various characteristics of the database that is preserved by such changes.
 +
 +The goal of the project is to extend the definition of differential privacy to Social Networks. In particular, to find proper measures of the information loss during anonymization.
 +
 +Requirements:​ Solid Probability theory background, Graph theory, Random graphs desirable.