ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data
By Michael Backes, Manuel Barbosa, Dario Fiore, Raphael M. Reischuk. 2015
109 citations recorded by the source corpus.
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 25x improvement in proof-computation time and a 20x reduction in prover storage space.
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
- Srinath T. V. Setty
- Dan Boneh
- Michael Walfish
- Eli Ben-Sasson
- Henry Corrigan-Gibbs
- Andrew Justin Blumberg
- Justin Thaler
- Eran Tromer
In Information Systems
- Dan Boneh
- Henry Corrigan-Gibbs
- Eran Tromer
- Srinath T. V. Setty
- Niv Gilboa
- Assa Naveh
- Eli Ben-Sasson
- Yuval Ishai
Citations in the corpus
- Zero-Knowledge Proofs on Secret-Shared Data via Fully Linear PCPs (2019, methodology): “This fact was implicitly used in previous proof systems on committed or secret-shared data [11,43,45].”
- Doubly-Efficient zkSNARKs Without Trusted Setup (2018, methodology): “proof (PCP)3 and ZK transform that form the basis of many recent zkSNARK implementations [4, 5, 12, 13, 15, 16, 29, 34, 36, 39, 42–44, 64, 76, 79, 105], including systems deployed in applications like ZCash [12, 107].”
- Efficient RAM and control flow in verifiable outsourced computation (2015, background): “[12] extend Pinocchio to efficiently support operations where the prover receives the input from a trusted third party and the verifier learns only the output.”
- Verifiable ASICs (2016, background): “The second category includes arguments: interactive arguments with preprocessing [99–101] and non-interactive arguments with preprocessing [24, 32, 36, 61, 76, 88].”
- Full Accounting for Verifiable Outsourcing (2017, background): “In the zkSNARK setting, the cost assessment depends on the premium that one is willing to pay for otherwise unachievable functionality [14, 18, 39].”
- Scalable Zero Knowledge Via Cycles of Elliptic Curves (2014, background): “Most zk-SNARK constructions [6,9,14,17,43,52,55,64,67–69,82,113,114], including published implementations [6,9,17,64,82,113,114], require expensive preprocessing during key generation.”
- PhotoProof: Cryptographic Image Authentication for Any Set of Permissible Transformations (2016, background): “[2] suggested ADSNARK, a proof system on authenticated data.”