Traceable PRFs: Full Collusion Resistance and Active Security

Maitra, Sarasij; Wu, David J.

doi:10.1007/978-3-030-97121-2_16

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Multiple key-homomorphic PRF families have been constructed via varying approaches [226,53,35,238,177,15]. In addition to key-homomorphism, PRF families have been defined/constructed with various other properties and features, e.g., bi-homomorphic PRFs [275], (private) constrained PRFs [75,55,73,83,57,246,244], Legendre PRFs [94], power residue PRFs [43], traceable PRFs [146,207], quantum PRFs [324,101], oblivious PRFs [84,56,124], domain-preserving PRFs [152], structure-preserving PRFs [2], related-key attack (RKA) secure PRFs [41,40,140,201,251,32,1,191,53,275,35], threshold/distributed PRFs [81,167,193,53,275,35], privately programmable PRFs [57,244], (zero-knowledge) provable PRFs [192,…”

Section: Definition 14 (Key-homomorphic Prf [53]mentioning

confidence: 99%

Star-specific Key-homomorphic PRFs from Linear Regression and Extremal Set Theory

Sehrawat¹,

Yeo²,

Vassilyev³

2022

Preprint

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We introduce a novel method to derandomize the learning with errors (LWE) problem by generating deterministic yet sufficiently independent LWE instances, that are constructed via special linear regression models. We also introduce star-specific key-homomorphic (SSKH) pseudorandom functions (PRFs), which are defined by the respective sets of parties that construct them. We use our derandomized variant of LWE to construct a SSKH PRF family. The sets of parties constructing SSKH PRFs are arranged as star graphs with possibly shared vertices, i.e., some pair of sets have non-empty intersections. We reduce the security of our SSKH PRF family to the hardness of LWE. To establish the maximum number of SSKH PRFs that can be constructed -by a set of parties -in the presence of passive/active and external/internal adversaries, we prove several bounds on the size of maximally cover-free at most t-intersecting k-uniform family of sets H, where the three properties are defined as:For the same purpose, we define and compute the mutual information between different linear regression hypotheses that are generated via overlapping training datasets.

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Section: Definition 14 (Key-homomorphic Prf [53]mentioning

confidence: 99%

Star-specific Key-homomorphic PRFs from Linear Regression and Extremal Set Theory

Sehrawat¹,

Yeo²,

Vassilyev³

2022

Preprint

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Simple Watermarking Pseudorandom Functions from Extractable Pseudorandom Generators

Bock,

Brzuska,

Lai

2024

IACR CiC

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Watermarking pseudorandom functions (PRF) allow an authority to embed an unforgeable and unremovable watermark into a PRF while preserving its functionality. In this work, we extend the work of Kim and Wu [Crypto'19] who gave a simple two-step construction of watermarking PRFs from a class of extractable PRFs satisfying several other properties – first construct a mark-embedding scheme, and then upgrade it to a message-embedding scheme. While the message-embedding scheme of Kim and Wu is based on complex homomorphic evaluation techniques, we observe that much simpler constructions can be obtained and from a wider range of assumptions, if we forego the strong requirement of security against the watermarking authority. Concretely, we introduce a new notion called extractable PRGs (xPRGs), from which extractable PRFs (without security against authorities) suitable for the Kim-Wu transformations can be simply obtained via the Goldreich-Goldwasser-Micali (GGM) construction. We provide simple constructions of xPRGs from a wide range of assumptions such as hardness of computational Diffie-Hellman (CDH) in the random oracle model, as well as LWE and RSA in the standard model.

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Traceable PRFs: Full Collusion Resistance and Active Security

Cited by 2 publications

References 37 publications

Star-specific Key-homomorphic PRFs from Linear Regression and Extremal Set Theory

Star-specific Key-homomorphic PRFs from Linear Regression and Extremal Set Theory

Simple Watermarking Pseudorandom Functions from Extractable Pseudorandom Generators

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