Secure Noise Sampling for DP in MPC with Finite Precision
Hannah Keller, Helen Möllering, Thomas H. Schneider, Oleksandr Tkachenko, Liang Zhao · 2024
While secure multi-party computation (MPC) protects the privacy of inputs and intermediate values of a computation, differential privacy (DP) ensures that the output itself does not reveal too much about individual inputs. For this purpose, MPC can be used to generate noise and add this noise to the output. However, securely generating and adding this noise is a challenge considering real-world implementations on finite-precision computers, since many DP mechanisms guarantee privacy only when noise is sampled from continuous distributions requiring infinite precision.