SecretFlow 1.14.0 Release: A Lighter, More Streamlined Experience 🚀

Niko zeng — Wed, 24 Sep 2025 06:29:39 +0000

We are excited to announce the official release of SecretFlow 1.14.0!

This version brings important optimizations and issue fixes that make SecretFlow easier to use and more developer-friendly.

🌐 What is SecretFlow?

SecretFlow ** is an **open-source privacy-preserving computing framework initiated by Ant Group and embraced by a fast-growing open-source community.

Its mission is to build a “usable but invisible” data computing infrastructure — enabling multiple parties to collaborate on data securely, without exposing their raw data.

🔑 Key Technologies Integrated in SecretFlow

MPC (Multi-Party Computation): allows multiple participants to jointly compute results without revealing their private inputs.
FL (Federated Learning): enables decentralized model training where data never leaves its original source.
HE (Homomorphic Encryption): makes it possible to perform computations directly on encrypted data.
TEE (Trusted Execution Environment): leverages hardware-based security enclaves to protect both data and computation.

🚀 Application Scenarios

SecretFlow has been applied in real-world scenarios where data security and compliance are critical:

Financial Risk Control: joint modeling between institutions to enhance anti-fraud and credit scoring.
Insurance Pricing: precise car insurance pricing based on combined human and vehicle data.
Healthcare Research: cross-hospital collaboration on medical studies without disclosing sensitive patient data.
Telecom & Operators: compliant data integration across multiple entities.

In short, SecretFlow provides the foundation for trusted collaboration on data in an era where both privacy and data value are equally important.

🔧 Optimizations in 1.14.0

secretflow_fl migrated to an independent repo sfl

This improves modularization, allowing the federated learning module to evolve independently.
Unified installation packages and images

From now on, there will no longer be separate secretflow and secretflow[lite] packages.

The Docker images are also unified — bringing you a simpler, lighter deployment experience.

🛠️ Bug Fixes

We also addressed community-reported issues in this release:

A big thanks to our community users who actively reported issues and helped us improve!

⭐ If you find SecretFlow useful, don’t forget to give us a Star on GitHub:

👉 https://github.com/secretflow/secretflow

Your support means a lot and helps the community grow stronger! 💪

How to Run Secure Multi-Party Computation in Python with SecretFlow

Niko zeng — Thu, 18 Sep 2025 10:10:44 +0000

The rise of privacy-preserving computation is driven by two major trends:

Stricter regulations: Laws like the EU’s_General Data Protection Regulation (GDPR)_and China’s_Data Security Law_and_Personal Information Protection Law_place strong requirements on protecting personal data.
Urgent business needs: Data silos across industries are stifling innovation. Privacy-preserving computation becomes the bridge between safeguarding privacy and unlocking the value of data.

By 2025, with the widespread adoption of*Generative AI, a new question has become central to the field of data privacy:Who owns the content generated by AI trained on user data?*How can data providers ensure their privacy is protected when their data powers these models?

To address these challenges, we introduce*SecretFlow— a modern, user-friendly framework for privacy-preserving computation that enables encrypted data to be computed securely. It embodies the principle of “usable but invisible*” when it comes to data privacy.

Press enter or click to view image in full size

📍 GitHub:https://github.com/secretflow/secretflow

The most surprising part? If you know*Python*, you can start using SecretFlow today to explore the future of privacy computing.

This hands-on tutorial will guide you step-by-step into this powerful technology, helping you find the perfect balance between*data security and data utility*.

Let’s begin our journey into the world of SecretFlow!

What is SecretFlow?

SecretFlowis a*trusted privacy-preserving computation framework, acting like a “security guard” for your data. It allows multiple parties to collaboratively compute and analyze datawithout ever revealing their private data*to each other.

With SecretFlow, different organizations can “play a game blindfolded” — working together efficiently, while never exposing their own sensitive data.

✅ Key Advantages

Unified and Integrated Built-in support for mainstream privacy-preserving technologies like Secure Multi-Party Computation (MPC), Federated Learning (FL), and Differential Privacy (DP) — no need to learn multiple frameworks.
Zero Learning Curve Native support for SQL, Python, and AI-friendly interfaces lets developers onboard quickly with minimal effort.
Modular & Flexible LEGO-like architecture allows you to plug and play based on business needs, speeding up iteration.
Proven High Performance Successfully deployed in financial and healthcare industries, with tested scalability on*billion-level data volumes*.

Real-World Use Cases

Medical Research Hospitals can conduct joint studies*without sharing raw patient data*, ensuring privacy during data analysis.
Financial Fraud Detection Banks can collaborate on fraud detection models*while protecting customer data*.
Cross-Company Collaboration Enterprises can co-analyze datasets without exposing internal data, improving the*efficiency and safety of cooperation*.
AI Model Training AI companies can train models on user data securely, with SecretFlow ensuring data privacy throughout the pipeline.

Quick Start: Run SecretFlow in Minutes

🚀 Official Docker Images Available

You can get started with one command using the official Docker images.

# Full version
docker run -it secretflow/secretflow-anolis8:latest

# Lite version (no deep learning, smaller size)
docker run -it secretflow/secretflow-lite-anolis8:latest

Secure Multi-Party Computation (MPC) Example

Let’s walk through a simple MPC example using SecretFlow: computing the*average income of three people*without revealing individual incomes.

What is MPC?

Secure Multi-Party Computation (MPC) is a cryptographic technique that allows multiple parties to jointly compute a function*without revealing their private inputs*.

Example:

Alice, Bob, and Carol want to calculate their*average salary, but none of them wants to disclose their exact income. MPC allows them to get the resultwithout exposing any personal data*.

Key Concepts

Party: An entity holding data and participating in computation.
Protocol: Rules ensuring secure computation.
Secret Sharing: Splitting sensitive data into fragments. Only with enough fragments can the original data be reconstructed.

Step-by-Step Demo: Secure Average Calculation

1. Initialize the Privacy Environment

import secretflow as sf

sf.init(
    parties={'Alice', 'Bob', 'Carol'},
    address='local'
)

2. Assign Devices to Each Party

Each participant will use a dedicated computing device to store their own data. We create devices for Alice, Bob, and Carol:

alice = sf.PYU('Alice')
bob = sf.PYU('Bob')
carol = sf.PYU('Carol')

3. Each Party Inputs Their Income Securely

To protect privacy, each participant enters their own income data through their own device:

\# Assume that Alice, Bob, and Carol have incomes of 5,000, 6,000, and 7,000 respectively.
alice_income = alice(lambda: 5000)()
bob_income = bob(lambda: 6000)()
carol_income = carol(lambda: 7000)()

Note: Each value only exists*within the assigned device*, invisible to others.

4. Use Secure Computation (SPU) to Compute the Average

spu = sf.SPU(sf.utils.testing.cluster_def(\['Alice', 'Bob', 'Carol'\]))
average_income = spu(lambda x, y, z: (x + y + z) / 3)(alice\_income, bob\_income, carol_income)

During this process:

Alice, Bob, and Carol’s income data is fed to the SPU device in an encrypted or secret-shared form.

The SPU device securely completes the computation, preventing the participants from accessing each other’s original data.

5. Reveal the Encrypted Result

Printing the result directly will show an encrypted object:

print("Average income is:", sf.reveal(average_income))
\# Output: 6000.0

At this point the data is still encrypted, so we need to use the sf.reveal method to securely decrypt and view the result: This demonstrates*data usability without visibility*— the very core of privacy-preserving computation.

\# Securely decrypt and view the result
print("The average income of the three people is:", sf.reveal(average_income))
\# Output: The average income of the three people is: 6000.0
\# That is, (5000 + 6000 + 7000) / 3 = 6000

SecretFlow System Architecture

SecretFlow’s layered architecture ensures modular, scalable privacy computing:

Abstract Device Layer Includes both public and secure computation devices (e.g., SPU, HEU).
Device Flow Layer Models algorithms as*device object streams*and DAGs.
Algorithm Layer Supports horizontal/vertical partitioned data for analytics and ML.
Workflow Layer Integrates data processing, model training, and hyperparameter tuning.

🔗 Related Ecosystem Projects

Kuscia: Lightweight task orchestration framework for privacy-preserving computation
SCQL: SQL-style engine for multi-party secure analysis
SPU: Secure computing backend for MPC
HEU: High-performance homomorphic encryption library
YACL: Core C++ library for crypto, networking, and IO

Think of SecretFlow as a*“smart privacy factory”*:

All raw materials (data) stay encrypted
Different workers (parties) collaborate safely
Modular and adaptable like LEGO
Fast and stable production line (high-performance computing)

Final Words

This article only scratches the surface of what*SecretFlow*can do.

Whether you’re a*developer,data scientist, orresearcher, SecretFlow makes it easy to embraceprivacy-preserving technologiesand unlockdata value without compromising privacy*.

If you love open source, give SecretFlow a ⭐️ on GitHub! Every star counts ❤️

👉 GitHub:https://github.com/secretflow/secretflow

How to Run Secure Multi-Party Computation in Python with SecretFlow

Niko zeng — Thu, 18 Sep 2025 10:07:27 +0000

Forem: Niko zeng

SecretFlow 1.14.0 Release: A Lighter, More Streamlined Experience 🚀

🌐 What is SecretFlow?

🔑 Key Technologies Integrated in SecretFlow

🚀 Application Scenarios

🔧 Optimizations in 1.14.0

🛠️ Bug Fixes

How to Run Secure Multi-Party Computation in Python with SecretFlow

What is SecretFlow?

✅ Key Advantages

Real-World Use Cases

Quick Start: Run SecretFlow in Minutes

🚀 Official Docker Images Available

Secure Multi-Party Computation (MPC) Example

What is MPC?

Key Concepts

Step-by-Step Demo: Secure Average Calculation

1. Initialize the Privacy Environment

4. Use Secure Computation (SPU) to Compute the Average

SecretFlow System Architecture

🔗 Related Ecosystem Projects

Final Words

How to Run Secure Multi-Party Computation in Python with SecretFlow