Verifiable delegation of computation on outsourced data
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
Verifying computations with state
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
Verifying the correctness of remote executions: from wild implausibility to near practicality
Proceedings of the 9th Workshop on Hot Topics in Dependable Systems
Towards privacy-preserving fault detection
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Verifiable network function outsourcing: requirements, challenges, and roadmap
Proceedings of the 2013 workshop on Hot topics in middleboxes and network function virtualization
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We consider interactive, proof-based verifiable computation: how can a client machine specify a computation to a server, receive an answer, and then engage the server in an interactive protocol that convinces the client that the answer is correct, with less work for the client than executing the computation in the first place? Complexity theory and cryptography offer solutions in principle, but if implemented naively, they are ludicrously expensive. Recently, however, several strands of work have refined this theory and implemented the resulting protocols in actual systems. This work is promising but suffers from one of two problems: either it relies on expensive cryptography, or else it applies to a restricted class of computations. Worse, it is not always clear which protocol will perform better for a given problem.We describe a system that (a) extends optimized refinements of the non-cryptographic protocols to a much broader class of computations, (b) uses static analysis to fail over to the cryptographic ones when the non-cryptographic ones would be more expensive, and (c) incorporates this core into a built system that includes a compiler for a high-level language, a distributed server, and GPU acceleration. Experimental results indicate that our system performs better and applies more widely than the best in the literature.