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Verifiable Fully Homomorphic Encryption

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arxiv 2301.07041 v2 pith:PW2HWIKA submitted 2023-01-17 cs.CR

classification cs.CR
keywords integrityfullyhomomorphicrangeworkaddressapplicationsattacks
verification ladder T0 review T1 audit T2 compute T3 formal

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Fully Homomorphic Encryption (FHE) is seeing increasing real-world deployment to protect data in use by allowing computation over encrypted data. However, the same malleability that enables homomorphic computations also raises integrity issues, which have so far been mostly overlooked. While FHEs lack of integrity has obvious implications for correctness, it also has severe implications for confidentiality: a malicious server can leverage the lack of integrity to carry out interactive key-recovery attacks. As a result, virtually all FHE schemes and applications assume an honest-but-curious server who does not deviate from the protocol. In practice, however, this assumption is insufficient for a wide range of deployment scenarios. While there has been work that aims to address this gap, these have remained isolated efforts considering only aspects of the overall problem and fail to fully address the needs and characteristics of modern FHE schemes and applications. In this paper, we analyze existing FHE integrity approaches, present attacks that exploit gaps in prior work, and propose a new notion for maliciously-secure verifiable FHE. We then instantiate this new notion with a range of techniques, analyzing them and evaluating their performance in a range of different settings. We highlight their potential but also show where future work on tailored integrity solutions for FHE is still required.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

    cs.CR 2025-05 conditional novelty 6.0 of 10

    The authors define dynamic noisy multi-client functional encryption, present the PRF-based inner-product scheme DyNo, and use it to train a differentially private logistic regression with claimed millisecond-level per...

  2. Teaching an Old Dog New Tricks: Verifiable FHE Using Commodity Hardware

    cs.CR 2024-12 conditional novelty 6.0 of 10

    Argos shows that an integrity-only hypervisor enclave signing a final transcript with a discrete TPM can make FHE evaluation verifiable against malicious servers, with reported single-digit percent overhead when the 1...

  3. Zero-Knowledge Proof Frameworks: A Systematic Survey

    cs.CR 2025-02 conditional novelty 5.0 of 10

    A systematic survey and reproducible benchmark of 25 open-source zero-knowledge proof frameworks, with Docker environments and performance comparisons on SHA-256 and matrix multiplication.

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