  In many social networks, one publishes information that one wants to reveal
(e.g., the photograph of some friends) together with information that may lead
to privacy breaches (e.g., the name of these people). One might want to hide
this sensitive information by encrypting it and sharing the decryption key only
with trusted people, but this might not be enough. If the cipher associated to
a face is always the same, correlation between the output of a face recognition
system and the cipher can give useful clues and help train recognizers to
identify untagged instances of the face. We refer to these as "correlation
attacks".
  In this paper we present a coding system that attempts to counter correlation
attacks by associating to each instance of a face a different encryption of the
same tag in such a way that the correlation between different instances is
minimal.
  In addition, we present a key distribution code that allows only the owner of
the images to encode the tags, but allows a group of trusted friends to decode
them.
