Security and privacy represent crucial requirements in different
scenarios as organizations and parties involved may not want to
disclose their own private information to each other. Assuring
adequate and verifiable security and privacy in these scenarios
faces various challenges. One such challenge is whether proposed
protocols can be used over public channels, like internet. Another
possible issue is whether the complete final result of a protocol
can be broadcasted to, or be received by all parties. Collusion
attacks can pose another security challenge in multiparty settings.
Finally, it may be desirable to design incremental versions of the
protocols to improve security and efficiency. To address these
problems we have designed new secure building blocks and
privacy-preserving protocols, while considering their performance
in terms of security and efficiency. Building blocks, and the
resulting protocols which take advantage of these blocks can be
implemented over public channels, have a balanced distribution of
the final results, and are resistant to collusion attacks. These
blocks are used to design novel privacy-preserving protocols for
learning and data mining techniques.
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