|Project Title||Using Secured MPC for Crypto Key protection and management|
|H2020 Topic List||Not specified yet|
|Role within the Consortium||* Project Partner|
|Type of activity||* Technology development|
|Project Description||Secure multiparty computation (MPC) addresses the problem of jointly computing a function among a set of |
mutually distrusting parties. It has a long history in the cryptographic literature, with its origins being found in
the literature in the mid 1980s. The basic scenario is that a group of parties wish to compute a given function on
their private inputs, while still keeping their inputs private from each other. For example, suppose that there are
three bankers Alice, Bob and Charlie, who wish to discover whose bonus was the largest that year, without
revealing what their actual bonuses were to each other or to a third party. To do so they engage in an
protocol, exchanging messages, with the result being the output of the desired function. The goal is that the
output of the protocol is just the value of the function, and nothing else is revealed. In particular, all that the
parties can learn is what they can learn from the output and their own input. So in the above example, the only
thing that the parties learn is that Charlie’s bonus was the highest; they do not know anything about the actual
bonus amounts, and do not know whether Alice’s bonus was higher or lower than Bob’s bonus.
Informally speaking, the most basic properties that a multi-party computation protocol aims to ensure are:
• Input privacy: The information derived from the execution of the protocol should not allow any inference
of the private data held by the parties, except for what is revealed by the prescribed output of the function.
• Correctness: Adversarially colluding parties willing to share information or deviate from the instructions
during the protocol execution should not be able to force honest parties to output an incorrect result.
There are a wide range of practical applications for multi-party computation, varying from simple tasks such as
coin tossing to more complex ones like electronic auctions (e.g. compute the market clearing price), electronic
voting, private DNA matching, privacy-preserving data mining, and more.
Secure multiparty computation can be leveraged to obtain a new paradigm of security: encryption of data while
in use. We are all familiar with the two basic paradigms of encryption: data-at-rest and data-in-motion.
However, encryption of data-in-use seems like an oxymoron: if data is encrypted, how is it possible to use it?
With secure computation this can be achieved. Consider the case that Alice holds an encryption key and Bob
holds an encrypted database, and the parties wish to run an SQL query on the database without ever decrypting
it. This exact problem can be cast as a two-input function, and thus can be securely computed; in this case,
input privacy means that the result of the SQL query is revealed and nothing else! Thus, SQL queries are
computed while the database is encrypted, thereby keeping the database secure, even while it is being used.