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CC BY 4.0 Open Benchmark Dataset · Frictionless Data Package

Open Cryptographic Benchmark Dataset & LSC1 Test Vectors

Download empirical throughput, memory utilization, and platform attachment-limit datasets in UTF-8 CSV, newline-delimited JSON (JSONL), and Frictionless Data Package format. Permanently archived at doi:10.7910/DVN/M6TLOS and doi:10.5281/zenodo.23671752.

Working Paper & Corpus (PDF)LSC1 Benchmarks (CSV) ↗LSC1 Benchmarks (JSONL) ↗2026 Attachment Limits (CSV) ↗datapackage.json ↗BibTeX (.bib) ↗

1. LSC1 WebCrypto Streaming vs. WebRTC P2P Benchmark Table

#ProtocolCipher / FramingFramePayloadNetworkThroughputPeak RAM
1LSC1-STREAMAES-256-GCM + HKDF-SHA2564 MiB1.0 GBFiber 1 Gbps842.4 Mbps48.2 MiB
2LSC1-STREAMAES-256-GCM + HKDF-SHA2564 MiB5.0 GBFiber 1 Gbps836.1 Mbps52.0 MiB
3LSC1-STREAMAES-256-GCM + HKDF-SHA2564 MiB50.0 GBFiber 10 Gbps1,940.8 Mbps64.5 MiB
4LimeDrop-WebRTCDTLS 1.3 SCTP DataChannel256 KiB5.0 GBLAN Wi-Fi 6 P2P915.0 Mbps34.1 MiB
5LSC1-Argon2idAES-256-GCM + Argon2id (64 MiB)4 MiB3.0 GB5G Sub-6312.6 Mbps96.4 MiB
6LSC1-DestructionHMAC-SHA256 Attested Erase4 MiB10.0 GBCloud R2 Zero-Egress860.2 Mbps44.0 MiB

2. Load Directly in Python, pandas, or Hugging Face datasets

import pandas as pd

# Load LimeShare LSC1 WebCrypto & WebRTC P2P Throughput Benchmarks (CC BY 4.0)
df_bench = pd.read_csv("https://limeshare.org/data/limeshare-lsc1-benchmarks.csv")
print(df_bench[["protocol", "payload_size_gb", "throughput_mbps", "peak_ram_mib"]])

# Or load with Hugging Face datasets:
from datasets import load_dataset
ds = load_dataset("json", data_files="https://limeshare.org/data/limeshare-lsc1-benchmarks.jsonl")

3. License & Attribution (CC BY 4.0)

All benchmark tables, protocol test vectors, and working papers on this page are released under Creative Commons Attribution 4.0 International (CC BY 4.0). Suggested attribution: “LimeShare Cryptography & Distributed Systems Lab (2026). LSC1 Protocol Specification & Zero-Knowledge File Transfer Benchmark Dataset. Harvard Dataverse (doi:10.7910/DVN/M6TLOS) & CERN Zenodo (doi:10.5281/zenodo.23671752). https://limeshare.org/data”

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