Privacy Stack 🔐 – 5000 Real arXiv Privacy Papers for Researchers & Builders

Ultimate Privacy Research Scraper – Converts peer‑reviewed privacy & anonymity papers into clean, reproducible, auditable datasets ready for analysis, dashboards, and code.

Live Actor: https://console.apify.com/oblate_wildcat/privacy-stack
GitHub: https://github.com/BikramBiswas786/privacy-stack

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💡 What Is Privacy Stack?

Privacy Stack is an Apify Actor that builds a large‑scale, high‑quality research corpus of real arXiv papers in security & privacy.

It scrapes and normalizes 5000 unique arXiv cs.CR papers across 4 critical categories, making it easy to explore, filter, and build on top of the latest privacy research without touching the arXiv UI. [web:20][file:13]

You get:

• A clean JSON/CSV dataset you can drop into analysis pipelines
• Strong deduplication guarantees
• A stable schema designed for LLMs, dashboards, and downstream tools

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📊 Categories (4 × 1250 Papers)

Each run targets exactly 1250 papers per category, for a total of 5000 unique cs.CR papers:

1. 🌐 Internet Privacy

   • Tor, mix networks, I2P, VPNs, onion routing
   • Traffic analysis attacks & defenses
   • Website fingerprinting, metadata‑hiding systems

2. 🔐 Crypto Privacy

   • Zero‑knowledge proofs (zk‑SNARKs, zk‑STARKs)
   • FHE, MPC, Bulletproofs, Pedersen commitments
   • Privacy coins (Zcash, Monero), mixer protocols, CoinJoin

3. 📊 Data Privacy

   • Differential privacy (local & global)
   • Federated learning, secure aggregation
   • Synthetic data, re‑identification resistance, anonymization

4. ⚛️ Post‑Quantum / PQ Security

   • Kyber, Dilithium, SPHINCS+, Falcon
   • Lattice‑based crypto, hash‑based signatures
   • PQ‑safe anonymous communication & key exchange

Each paper is tagged with a primary category plus the full arXiv category string, so you can slice the dataset however you want. [file:13]

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🚀 Key Features

• ✅ 100% real arXiv papers
  Directly scraped from arxiv.org cs.CR – no synthetic titles, no hallucinations, no fake IDs.

• ✅ 5000 UNIQUE papers
  Global deduplication by arXiv ID, plus per‑category deduplication so the same paper is never counted twice within a category.

• ✅ Balanced categories
  1250 papers for each of the 4 categories → balanced training/test sets for ML and fair comparisons between research areas.

• ✅ Production‑grade dataset schema
  Designed for:

  • LLM context building
  • dashboards (Grafana/Metabase/Superset)
  • offline analytics (Python/pandas, DuckDB, BigQuery)

• ✅ Zero manual setup on Apify
  No requirements.txt needed – runs on Apify’s managed Python runtime.

• ✅ Repeatable & auditable
  Same input → same structure, easy to diff across runs as new papers appear on arXiv.

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🧱 Dataset Schema

Each paper in the dataset has a consistent JSON structure:

{
  "id": 1,
  "title": "Device-Independent Anonymous Communication",
  "arxiv_id": "2512.21047",
  "full_category": "cs.CR (Internet Privacy)",
  "short_category": "internet_privacy",
  "authors": ["John Doe", "Jane Smith"],
  "url": "https://arxiv.org/abs/2512.21047",
  "pdf_url": "https://arxiv.org/pdf/2512.21047.pdf",
  "is_real_arxiv": true,
  "published": "2025-12-21",
  "updated": "2025-12-23",
  "abstract": "We propose a device-independent protocol for anonymous communication...",
  "source_run_id": "RUN_ID_FOR_AUDIT"
}

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📊 Apify Console Output Tabs

When you run Privacy Stack in Apify Console, the Output tab is split into multiple views (using dataset schema): [file:15]

• 📚 All Papers (5000) – full corpus merged
• 🌐 Internet Privacy (1250) – Tor, mixnets, I2P, traffic analysis
• 🔐 Crypto Privacy (1250) – ZK, FHE, MPC, crypto protocols
• 📊 Data Privacy (1250) – DP, FL, anonymization, re‑identification
• ⚛️ Post‑Quantum (1250) – Kyber, Dilithium, PQ anonymous systems
• 📋 Live Logs – scrape progress, dedup stats, category counts

Each view is sortable & filterable directly in the Apify Console, and also accessible as CSV/JSON via API.

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📥 Sample Output Snippet

{
  "id": 42,
  "title": "Traffic Analysis Resistant Mix Networks for the Modern Internet",
  "arxiv_id": "2507.12345",
  "full_category": "cs.CR (Cryptography and Security)",
  "short_category": "internet_privacy",
  "authors": ["Alice Anon", "Bob Mixnet"],
  "url": "https://arxiv.org/abs/2507.12345",
  "pdf_url": "https://arxiv.org/pdf/2507.12345.pdf",
  "is_real_arxiv": true
}

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⚙️ How It Works (High‑Level)

1. Input: categories + maximum papers per category (defaults to 1250 × 4).
2. Fetch arXiv feeds / search results for each category (cs.CR + keywords / sub-tags).
3. Normalize results into the unified schema:
   • parse title, authors, IDs, URLs, dates, category strings
4. Deduplicate:
   • global deduplication by arxiv_id
   • ensure each category’s slice has only unique entries
5. Store into Apify Dataset with multiple views (all + per‑category).

The Actor is designed to be idempotent in terms of structure, but you will naturally see newer papers when you re‑run it over time.

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🚀 Quick Start

1. Run from Apify Console

1. Open: Privacy Stack Actor
   https://console.apify.com/oblate_wildcat/privacy-stack
2. Set input (optional):
   • maxPapersPerCategory: default 1250
   • category toggles (if you want only 1–2 categories)
3. Click Run
4. When it finishes, open the Output tab:
   • Browse All Papers
   • Or switch to specific category views
5. Export as:
   • JSON (items?clean=true)
   • CSV (items?format=csv)
   • HTML table (for quick browsing)

2. Run via Apify CLI

apify run privacy-stack-research-scraper

This will:

• run the Actor locally
• store dataset in ./storage/datasets/default/
• you can then inspect OUTPUT.json or CSV in that folder.

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🧪 Example: Using the Dataset in Python

import requests
import pandas as pd

DATASET_URL = "https://api.apify.com/v2/datasets/<DATASET_ID>/items?clean=true"

res = requests.get(DATASET_URL)
res.raise_for_status()
items = res.json()

df = pd.DataFrame(items)

# Example: show recent ZK papers
zk_df = df[df['title'].str.contains("zero-knowledge", case=False, na=False)]
print(zk_df[['title', 'arxiv_id', 'url']].head())

# Example: count papers per short_category
print(df['short_category'].value_counts())

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🧠 Typical Use Cases

• Literature review for PhD / MSc / paper writing
  Quickly get 5000+ relevant cs.CR papers organized by topical area.

• Benchmark building
  Curate evaluation sets for LLMs, anonymization tools, or privacy frameworks.

• Trend analysis
  See how research volume changes over time in areas like ZK proofs or post‑quantum crypto.

• Dataset for downstream models
  Use title + abstract as input for topic modeling, embeddings, or semantic search.

• Meta‑research
  Study the evolution of anonymity, privacy‑preserving ML, and PQ crypto.

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🔐 Design Principles

• Real papers only – every record must correspond to a real arXiv entry.
• Transparent scraping – URLs always point back to arxiv.org.
• No guessing / hallucinating metadata – if arXiv does not provide it, it is not faked.
• Reproducibility – input + time window → deterministically shaped dataset schema.

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📦 Actor Input (Suggested Schema)

Typical input fields (simplified):

{
  "maxPapersPerCategory": 1250,
  "includeInternetPrivacy": true,
  "includeCryptoPrivacy": true,
  "includeDataPrivacy": true,
  "includePostQuantum": true
}

You can extend this in future (e.g., year range, specific arXiv query strings, exclusion filters).

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🧑‍💻 About the Author

Bikram Biswas (@BikramBiswas786)

• Quantum & privacy tooling developer
• Creator of Anon Lab (interactive privacy paper explorer)
• Active on Apify building research‑grade Actors for security, privacy, and data aggregation.

Apify profile: https://apify.com/bikrambiswas

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📄 Citation

If Privacy Stack helps in your work, you can cite it as:

@software{biswas2025privacystack,
  author = {Biswas, Bikram},
  title = {Privacy Stack: 5000 Real arXiv Privacy Papers for Researchers},
  year = {2025},
  url = {https://apify.com/oblate_wildcat/privacy-stack}
}

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📝 License & Ethics

• Use this dataset responsibly.
• All papers belong to their respective authors and arXiv.
• This Actor only organizes metadata and links; it does not strip or redistribute paywalled content.

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Privacy Stack turns scattered security & privacy literature into a single, structured research surface you can actually build on.
Run it, export it, and plug it straight into your research pipeline.