Cryptology ePrint Archive: Report 2014/617

ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data

Michael Backes and Manuel Barbosa and Dario Fiore and Raphael M. Reischuk

Abstract: We study the problem of privacy-preserving proofs on authenticated data, where a party receives data from a trusted source and is requested to prove computations over the data to third parties in a correct and private way, i.e., the third party learns no information on the data but is still assured that the claimed proof is valid. Our work particularly focuses on the challenging requirement that the third party should be able to verify the validity with respect to the specific data authenticated by the source — even without having access to that source. This problem is motivated by various scenarios emerging from several application areas such as wearable computing, smart metering, or general business-to-business interactions. Furthermore, these applications also demand any meaningful solution to satisfy additional properties related to usability and scalability. In this paper, we formalize the above three-party model, discuss concrete application scenarios, and then we design, build, and evaluate ADSNARK, a nearly practical system for proving arbitrary computations over authenticated data in a privacy-preserving manner. ADSNARK improves significantly over state-of-the-art solutions for this model. For instance, compared to corresponding solutions based on Pinocchio (Oakland’13), ADSNARK achieves up to 25× improvement in proof computation time and a 20× reduction in prover storage space.

Category / Keywords: privacy, zero-knowledge, proof systems

Date: received 12 Aug 2014, last revised 17 Nov 2014

Contact author: reischuk at inf ethz ch

Available format(s): PDF | BibTeX Citation

Version: 20141117:190106 (All versions of this report)

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