paper-with-me

Papers

Verifiable Homomorphic Linear Combinations in Multi-Instance Time-Lock Puzzles

2024-08-22 · Aydin Abadi

Time-Lock Puzzles (TLPs) have been developed to securely transmit sensitive information into the future without relying on a trusted third party. Multi-instance TLP is a scalable variant of TLP that enables a server to efficiently find solutions to different puzzles provided by a client at once. Nevertheless, existing multi-instance TLPs lack support for (verifiable) homomorphic computation. To address this limitation, we introduce the "Multi-Instance partially Homomorphic TLP" (MH-TLP), a multi-instance TLP supporting efficient verifiable homomorphic linear combinations of puzzles belonging to a client. It ensures anyone can verify the correctness of computations and solutions. Building on MH-TLP, we further propose the "Multi-instance Multi-client verifiable partially Homomorphic TLP" (MMH-TLP). It not only supports all the features of MH-TLP but also allows for verifiable homomorphic linear combinations of puzzles from different clients. Our schemes refrain from using asymmetric-key cryptography for verification and, unlike most homomorphic TLPs, do not require a trusted third party. A comprehensive cost analysis demonstrates that our schemes scale linearly with the number of clients and puzzles.

📄 PDF Abstract BibTeX arXiv:2408.12444

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination

2024-06-21 · Aydin Abadi

To securely transmit sensitive information into the future, Time-Lock Puzzles (TLPs) have been developed. Their applications include scheduled payments, timed commitments, e-voting, and sealed-bid auctions. Homomorphic T…

Federated Learning

Homomorphic Sensing of Subspace Arrangements

2020-06-09 · Liangzu Peng, Manolis C. Tsakiris

Homomorphic sensing is a recent algebraic-geometric framework that studies the unique recovery of points in a linear subspace from their images under a given collection of linear maps. It has been successful in interpret…

Missing ValuesRetrieval

SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT

2022-03-22 · Jayasree Sengupta, Sushmita Ruj, Sipra Das Bit

Recently collaborative learning is widely applied to model sensitive data generated in Industrial IoT (IIoT). It enables a large number of devices to collectively train a global model by collaborating with a server while…

Privacy Preserving

Instance-based Explanations for Gradient Boosting Machine Predictions with AXIL Weights

2023-01-05 · Paul Geertsema, Helen Lu

We show that regression predictions from linear and tree-based models can be represented as linear combinations of target instances in the training data. This also holds for models constructed as ensembles of trees, incl…

Feature Importanceregression

A Generative Product-of-Filters Model of Audio

2013-12-20 · Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the cl…

modelSpeaker Identification