Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers

By Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha. 2019

103 citations recorded by the source corpus.

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data analysis, which is typically implemented either in the "central" or "local" model. The local model has been more popular for commercial deployments as it does not require a trusted data collector. This increased privacy, however, comes at the cost of utility and algorithmic expressibility as compared to the central model. In this work, we propose, Cryptε, a system and programming framework that (1) achieves the accuracy guarantees and algorithmic expressibility of the central model (2) without any trusted data collector like in the local model. Cryptε achieves the "best of both worlds" by employing two non-colluding untrusted servers that run DP programs on encrypted data from the data owners. In theory, straightforward implementations of DP programs using off-the-shelf secure multi-party computation tools can achieve the above goal. However, in practice, they are beset with many challenges like poor performance and tricky security proofs. To this end, Cryptε allows data analysts to author logical DP programs that are automatically translated to secure protocols that work on encrypted data. These protocols ensure that the untrusted servers learn nothing more than the noisy outputs, thereby guaranteeing DP (for computationally bounded adversaries) for all Cryptε programs. Cryptε supports a rich class of DP programs that can be expressed via a small set of transformation and measurement operators followed by arbitrary post-processing. Further, we propose performance optimizations leveraging the fact that the output is noisy. We demonstrate Cryptε's practical feasibility with extensive empirical evaluations on real world datasets.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tianhao Wang
2. Ninghui Li
3. Somesh Jha
4. Jingren Zhou
5. Bolin Ding
6. Cheng Hong
7. Min Xu
8. Zhicong Huang

In Information Systems

1. Tianhao Wang
2. Ninghui Li
3. Somesh Jha
4. Bolin Ding
5. Jingren Zhou
6. Zhicong Huang

Citations in the corpus

• Improving utility and security of the shuffler-based differential privacy (2019, background): “One notable highlight is [22], which proposes Crypt .”
• Practical and Robust Privacy Amplification with Multi-Party Differential Privacy (2019, methodology): “[19] proposes one instantiation of such an idea via homomorphic encryption, and we call it HE for short.”