Secure and private online collaboration
In this thesis, we investigate confidentiality- and privacy-preserving protocols. Confidentiality- and privacy-preserving protocols (also called secure protocols) allow two or more parties to compute some function of their private inputs without revealing to any group of the parties information other than the cooperatively computed output and what can be deduced from this output and the group's individual inputs (which is unavoidable, as it is inherently part of any such protocol). It has been shown previously that any function can be computed in such a manner; the study of computing any function securely is called Secure Multiparty Computation (SMC) or Secure Function Evaluation (SFE). However, before applying these techniques to a specific domain, one first has to identify problems where secure protocols are useful. When there is a situation where a secure protocol is needed, there will always be a secure protocol for computing the function by the general results described in the SMC literature. However, the general solutions are complex and in many cases there are more efficient domain-specific solutions. In this thesis we look at several application domains including: trust negotiation, credit checking, services for location-aware devices, contract negotiation, and secure biometric authentication. In these domains we identify situations where secure protocols are useful and then develop simple and efficient protocols for these situations. ^
Major Professor: Mikhail J. Atallah, Purdue University.
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