FPSI protocols based on secret-shared OPRF achieve linear complexity and 9-145x speedups over state-of-the-art for L_p distance metrics.
Space/time trade-offs in hash coding with allowable errors
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A unified threat model and evaluation framework is developed to compare privacy-preserving methods for distributed learning in IoT, showing trade-offs in privacy robustness and system efficiency with Bloom filter encodings highlighted for low overhead.
SNNF uses an event-based binary image and single-layer SNN to achieve 0.89 AUC in distinguishing signal from noise in DVS while using only 11-40% of the resources of prior filters.
citing papers explorer
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Efficient Fuzzy Private Set Intersection from Secret-shared OPRF
FPSI protocols based on secret-shared OPRF achieve linear complexity and 9-145x speedups over state-of-the-art for L_p distance metrics.
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Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
A unified threat model and evaluation framework is developed to compare privacy-preserving methods for distributed learning in IoT, showing trade-offs in privacy robustness and system efficiency with Bloom filter encodings highlighted for low overhead.
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SNNF: An SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors
SNNF uses an event-based binary image and single-layer SNN to achieve 0.89 AUC in distinguishing signal from noise in DVS while using only 11-40% of the resources of prior filters.