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ALITHEIA: Towards Practical Verifiable Graph Processing

Published: 03 November 2014 Publication History

Abstract

We consider a scenario in which a data owner outsources storage of a large graph to an untrusted server; the server performs computations on this graph in response to queries from a client (whether the data owner or others), and the goal is to ensure verifiability of the returned results. Existing work on verifiable computation (VC) would compile each graph computation to a circuit or a RAM program and then use generic techniques to produce a cryptographic proof of correctness for the result. Unfortunately, such an approach will incur large overhead, especially in the proof-computation time. In this work we address the above by designing, building, and evaluating ALITHEIA, a nearly practical VC system tailored for graph queries such as computing shortest paths, longest paths, and maximum flow. The underlying principle of ALITHEIA is to minimize the use of generic VC systems by leveraging various algorithmic techniques specifically for graphs. This leads to both theoretical and practical improvements. Asymptotically, it improves the complexity of proof computation by at least a logarithmic factor. On the practical side, we show that ALITHEIA achieves significant performance improvements over current state-of-the-art (up to a 108x improvement in proof-computation time, and a 99.9% reduction in server storage), while scaling to 200,000-node graphs.

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      cover image ACM Conferences
      CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
      November 2014
      1592 pages
      ISBN:9781450329576
      DOI:10.1145/2660267
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      Published: 03 November 2014

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      Author Tags

      1. cloud computing
      2. graph processing
      3. verifiable computation

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      • (2024)AegisDB: Scalable Blockchain Database with Secure Decentralised Load BalancingInformation Security and Privacy10.1007/978-981-97-5101-3_6(105-119)Online publication date: 15-Jul-2024
      • (2023)Reliable Data Provenance in HCNBlockchain-Based Data Security in Heterogeneous Communications Networks10.1007/978-3-031-52477-6_3(57-90)Online publication date: 19-Dec-2023
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