![]() To help draw a distinction between the two approaches, we will discuss the power and limitations of both and give typical scenarios in which each can be highly effective. However, because they are both aimed at using private information without fully revealing it, they are often confused. MPC and DP were invented to address different real-world problems and to achieve different technical goals. ![]() We will revisit two well-studied approaches to this challenge: secure multiparty computation (MPC) and differential privacy (DP). Technique that mixes public and private training data can meet differential-privacy criteria while cutting error increase by 60%-70%. ![]()
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