[PET] Postdoc position at UniGE (University of Geneva)

[Claudia Diaz]

Forwarding in case anyone on this list is interested in applying to this position. 


> *Subject:* Reliable content-based access of multimedia repositories
> 
> *Keywords:* multimedia, privacy, outsourcing
> 
> Outsourcing  multimedia data  is what  million of  users do  everyday:
> people share their  images on Picasa, on Flickr, on  Facebook or other
> servers. Today, such  data is accessed via keywords  or tags. Academic
> prototypes already propose content-image search to some extent and, in
> a very  near future,  these technologies will  reach a  level allowing
> users to  query large image repositories  over the Web. This,  to some
> extent, already happens with the  Google Search by Image service. Such
> services for multimedia data can only flourish as the general trend of
> cloud computing fosters outsourcing.
> 
> These  systems  create  obvious, yet  complicated,  privacy-preserving
> problems.  With the  current  technologies,  it is  very  easy for  an
> outsourced  service to  identify what  a user  is interested  into, by
> simply checking  what queries are  submitted to the system.  When such
> queries concern  sensitive information  (medical images  for example),
> then, some knowledge  can be inferred which  might compromise privacy.
> Worse, outsourced services can correlate  queries issued by many users
> and subsequently  define groups of  users sharing some  similarity, or
> secretly mine queries to detect patterns and trends.
> 
> The  goal of  this  project  is to  propose  strategies  to make  more
> difficult  such   privacy  breaches  in  the   context  of  outsourced
> content-based  image  retrieval  systems.   While  there  are  obvious
> anonymization  actions  to  take, more  sophisticated  techniques  are
> likely to be needed.
> 
> Overall, there is a rather long list of possible scenarios that differ
> by the  data that  is exchanged between  the various  parties involved
> here, by the  level of knowledge each  can have, or by  the goal which
> might be to preclude the identification of a user and/or the discovery
> of correlations between users.
> 
> Few  case-studies   can  already   be  foreseen   as  a   context  for
> investigating privacy  preservation in the content-based  image search
> and access.  These range from  searches based on example  query images
> (either sent directly, or as low-level features or even encoded with a
> matched dictionary) to accesses made via browsing and navigation.
> 
> A classical solution consists in sending  the actual query with a flow
> of  unrelated queries,  so as  to  prevent distinguishing  what is  of
> interest to the  user or not. Determining what  the distractors should
> be, their number,  their diversity and how to filter  the many answers
> received back from the server in  order to isolate the true result are
> part  of the  problems to  study.   Further questions  related to  the
> induced    increase    in    complexity    along    the    chain    of
> client-network-server need  to be answered,  in relation to  the exact
> scenario that is  proposed (eg mobile content access  with thin client
> and limited bandwidth).  Even further, questions related  to whether a
> single user or a group of users is considered should also be answered.
> Similarly, the type of queries  (eg very specific rare queries against
> quite generic frequent  queries) may be explored.   Finally, the level
> of shared  knowledge (eg  content of  the repositories,  user expected
> profiles) is a further parameter to  be accounted for in the design of
> a privacy preserving strategy.
> 
> Overall, the project can be  summarized by investigating what type and
> level of “noise” should be added  to the original queries, and how the
> superset  of  answers  should  be filtered  to  recover  the  targeted
> results.
> 
> This work  is a tight collaboration  between Stéphane Marchant-Maillet
> (U. Geneva,  CH) and Ewa  Kijak, Teddy Furon, Laurent  Amsaleg (IRISA,
> FR)
> 
> *Position:*
> - Postdoc position at UniGE (University of Geneva) asap until end of 2013.
> - Work in Geneva, Switzerland. Exchange with IRISA Rennes, France.
> 
> *Expertise:* information retrieval (search and protocols), data mining
> and machine learning.
> 
> *Application.* Please send your full CV as well as references to:
> - Laurent Amsaleg (laurent.amsaleg at irisa.fr)
> - Stéphane Marchant-Maillet (stephane.marchand-maillet at unige.ch)
>