Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

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

9 citations recorded by the source corpus.

How researchers see this paper

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

1. Tianhao Wang
2. Somesh Jha
3. Ninghui Li
4. Min Xu
5. Bolin Ding
6. Jingren Zhou
7. Edo Roth
8. Lingjuan Lyu

Citations in the corpus

• Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “Outis [21] similarly supports GDP without a trusted party but requires two non-colluding semi-honest servers, with their cryptographic server serving as an analogue to Honeycrisp’s committee functionality.”
• Practical and Robust Privacy Amplification with Multi-Party Differential Privacy (2019, methodology): “This is typically achieved by introducing additional parties [7,15,16,23].”
• Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning (2020, background): “applications as it constitutes a single point of failure for data breaches, and saddles the trusted curator with legal and ethical obligations to keep the user data secure [1].”