Multi-Party Computation (Mpc)
| Multi-Party Computation (MPC) is a research method that allows for maintaining data anonymity. It is a type of cryptography that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. MPC has a wide range of applications, from electronic voting to secure auctions. MPC is a relatively new field, with the first papers on the topic being published in the early 1990s. In the past few years, there has been a surge of interest in MPC, both from a theoretical and a practical perspective. This is due in part to the increasing importance of privacy in the digital age. With the advent of big data, there is a growing need for methods that allow for the analysis of sensitive data while preserving the privacy of individuals. MPC is one of the few methods that allows for this type of privacy-preserving computation. MPC is based on a simple idea: instead of having a single party compute a function over private data, we can have multiple parties jointly compute the function. Each party holds a piece of the input data, and the function is computed by combining the inputs from all of the parties. Importantly, no single party ever learns the entire input data. This allows for the data to be kept private. MPC has a number of advantages over other privacy-preserving methods. First, MPC is scalable. It can be used to jointly compute functions over inputs of any size. Second, MPC is efficient. The time and space complexity of MPC algorithms are comparable to those of their non-private counterparts. Finally, MPC is composable. This means that it can be used to build more complex privacy-preserving protocols by combining simpler MPC protocols. MPC is a powerful tool for preserving privacy in the digital age. It is scalable, efficient, and composable, making it well-suited for a wide range of applications. |
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