Differential privacy (DP) has arisen as the state-of-the-art metric for quantifying individual privacy when sensitive data are analyzed, and it is starting to see practical deployment in organizations such as the US Census Bureau, Apple, Google, etc. There are two popular models for deploying differential privacy - standard differential privacy (SDP), where a trusted server aggregates all the data and runs the DP mechanisms, and local differential privacy (LDP), where each user perturbs their own data and perturbed data is analyzed. Due to security concerns arising from aggregating raw data at a single server, several real world deployments in industry have embraced the LDP model. However, systems based on the LDP model tend to have poor utility - a gap in the utility achieved as compared to systems based on the SDP model. In this work, we survey and synthesize emerging directions of research at the intersection of differential privacy and cryptography. First, we survey solutions that combine cryptographic primitives like secure computation and anonymous communication with differential privacy to give alternatives to the LDP model that avoid a trusted server as in SDP but close the gap in accuracy. These primitives introduce performance bottlenecks and necessitate efficient alternatives. Second, we synthesize work in an area we call DP-Cryptography - cryptographic primitives that are allowed to leak differentially private outputs. These primitives have orders of magnitude better performance than standard cryptographic primitives. DP-cryptographic primitives are perfectly suited for implementing alternatives to LDP, but are also applicable to scenarios where standard cryptographic primitives do not have practical implementations. Through this unique lens of research taxonomy, we survey ongoing research in these directions while also providing novel directions for future research.
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