Lea Kissner
Privacy-Preserving Distributed Information Sharing
July 5, 2005
10:30 AM
Wean 5409
ABSTRACT:
In many important applications, a collection of mutually distrustful
parties must share information, without compromising their privacy. In
order to protect this private data, the players perform
privacy-preserving computation; that is, no party learns more
information about other parties’ private inputs than what can be
deduced from the result. Currently, these applications are often
performed by using some form of a trusted third party (TTP); this TTP
receives all players’ inputs, computes the desired function, and
returns the result. However, the level of trust that must be placed in
such a TTP is often inadvisable, undesirable, or even illegal. For
example, in many countries there are complex and restrictive laws
governing the use of personal medical data. In addition, the high level
of trust placed in a TTP makes it an appealing target for attack by
malicious parties. In order to make many applications practical and
secure, we must remove the TTP, replacing it with efficient protocols
for privacy-preserving distributed information sharing. My thesis will
answer the question of whether it is possible for mutually distrustful
parties to efficiently share information about large, distributed
bodies of data, without compromising their privacy.
Thesis proposal document: http://www.cs.cmu.edu/~leak/proposal.pdf
THESIS COMMITTEE:
Dawn Song, Chair
Manuel Blum
Dan Boneh, Stanford
Benny Pinkas, HP Labs
Michael Reiter
