Secure Multi-Party Computation (SMPC) is an important subset of cryptography. It has the potential to enable real data privacy. SMPC seeks to find ways for parties to jointly compute a function using their inputs, while keeping these inputs private.
Shamirâs Secret Sharing Scheme:
To understand SMPC, we must first understand Shamirâs Secret Sharing Scheme. The Schemeâs purpose is to divide and distribute one secret value over several nodes or users, so that no one knows anything about the secret value. To retrieve the secret value, a minimum quorum of users must pool their data together.
Shamirâs Secret Sharing Scheme can also be used to perform computations on a secret shared value. When we group together the results of each userâs computations on their respective pieces of data, we arrive at the outcome of the computationâââwithout knowing the secret inputs.
An Example
To better understand this concept, letâs use a simple example: a secret number that is larger than 1000, a hundred users within a system, and a quorum of 10Â users.
Each user is given a unique number between 100 and 199.
Since the minimum quorum requirement is 10 users, any time a group of 10 or more people come together, their combined numbers, x, will reveal the secret ( x > 1,000 ) without revealing any personâs individual number.
Secure Multi-Party Computation on Personal Data
Now, instead of using a number, letâs say the âsecretâ is a userâs personal data.
SMPC works in much the same way: the personal data is split into several, smaller parts, each of which is masked using cryptographic techniques. Next, each small, encrypted piece of data is sent to a separate, independent server, so that each server only contains a small part of the data.
An individual or organization looking to discover the âsecretâ (i.e., uncover the personal data) will need to aggregate the encoded data. Additionally, it will be possible to perform computations based on the personal data, by requiring each server to perform computations on its small part of the data, without disclosing the data.
Implications for Privacy
People have recently started demanding that governments and companies safeguard their personal information more proactively, especially financial, health, demographic, or other sensitive information. Large organizations are now looking to maintain their customersâ trust by being responsible guardians of their personal data.
In these circumstances, SMPCâs combination of encryption, distribution, and distributed computation can have a profound impact on data privacy and security.