Differential Privacy meets Verifiable Computation: Achieving Strong Privacy and Integrity Guarantees
Paper in proceeding, 2019
In this setting, service providers want guarantees about the correctness of the computations, while individuals want guarantees that their sensitive information will remain private. Encryption mechanisms are not sufficient to avoid any leakage of information, since querying a database about individuals or requesting summary statistics can lead to leakage of information. Differential privacy addresses the paradox of learning nothing about an individual, while learning useful information about a population. Verifiable computation addresses the challenge of proving the correctness of computations. Although verifiable computation and differential privacy are important tools in this context, their interconnection has received limited attention. In this paper, we address the following question: How can we design a protocol that provides both differential privacy and verifiable computation guarantees for outsourced computations? We formally define the notion of verifiable differentially private computation (VDPC) and what are the minimal requirements needed to achieve VDPC. Furthermore, we propose a protocol that provides verifiable differentially private computation guarantees and discuss its security and privacy properties.
Verifiable Computation
Privacy-preservation
Differential Privacy
Author
Georgia Tsaloli
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
Aikaterini Mitrokotsa
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
ICETE 2019 - Proceedings of the 16th International Joint Conference on e-Business and Telecommunications
Vol. 2 425-430
978-989-758-378-0 (ISBN)
Prague, Czech Republic,
Areas of Advance
Information and Communication Technology
Subject Categories
Information Science
Computer Science
Computer Systems
DOI
10.5220/0007919404250430