LatticeX-Foundation/Rosetta

• highreadme#1

  Add a disambiguation statement to the README overview

  Why:

  CURRENT

  Rosetta is a privacy-preserving framework based on TensorFlow. It integrates with mainstream privacy-preserving computation technologies, including cryptography, federated learning and trusted execution environment. Rosetta aims to provide privacy-preserving solutions for artificial intelligence without requiring expertise in cryptography, federated learning and trusted execution environment. Rosetta reuses the APIs of TensorFlow and allows to transfer traditional TensorFlow codes into a privacy-preserving manner with minimal changes. E.g., just add the following line.

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  LatticeX-Foundation/Rosetta is a privacy-preserving framework based on TensorFlow. **Important Note: This project is NOT related to the blockchain Rosetta API.** It integrates with mainstream privacy-preserving computation technologies, including cryptography, federated learning and trusted execution environment. Rosetta aims to provide privacy-preserving solutions for artificial intelligence without requiring expertise in cryptography, federated learning and trusted execution environment. Rosetta reuses the APIs of TensorFlow and allows to transfer traditional TensorFlow codes into a privacy-preserving manner with minimal changes. E.g., just add the following line.

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• mediumreadme#2

  Add a 'Why Choose Rosetta?' or 'Comparison' section to the README

  Why:

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  ## Why Choose Rosetta?
  Rosetta offers a unique advantage for TensorFlow users seeking privacy-preserving AI, enabling adaptation of existing models with minimal code changes. Unlike frameworks focused solely on federated learning (e.g., TensorFlow Federated, OpenFL) or differential privacy (e.g., TensorFlow Privacy), Rosetta integrates a broader spectrum of secure computation technologies, including secure multi-party computation (SecureNN, Helix for 3 parties) and efficient zero-knowledge proofs (Mystique for secure inference of complex models like ResNet). This comprehensive approach allows developers to leverage advanced cryptographic techniques without deep expertise, directly within their TensorFlow workflows.

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• lowabout#3

  Enhance the repository's 'About' description

  Why:

  CURRENT

  A Privacy-Preserving Framework Based on TensorFlow

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  A Privacy-Preserving Framework for AI based on TensorFlow, integrating secure multi-party computation (SMPC) and efficient zero-knowledge proofs (ZKP) for secure inference and training.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I adapt existing TensorFlow models for privacy-preserving AI applications?

  you: not recommended
  AI recommended (in order):

  1. TensorFlow Federated
  2. TensorFlow Privacy
  3. PySyft
  4. OpenFL
  5. IBM's Federated Learning Library

  AI recommended 5 alternatives but never named LatticeX-Foundation/Rosetta. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What are frameworks for secure multi-party computation in machine learning, compatible with Python?

  you: not recommended
  AI recommended (in order):

  1. PySyft (OpenMined/PySyft)
  2. MP-SPDZ (data61/MP-SPDZ)
  3. FRESCO (FRESCO-MPC/FRESCO)
  4. Conclave (Opaque-Systems/conclave)
  5. TensorFlow Privacy (tensorflow/privacy)
  6. CrypTen (facebookresearch/CrypTen)

  AI recommended 6 alternatives but never named LatticeX-Foundation/Rosetta. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of LatticeX-Foundation/Rosetta?

  pass
  ✓AI named LatticeX-Foundation/Rosetta explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts LatticeX-Foundation/Rosetta in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named LatticeX-Foundation/Rosetta explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo LatticeX-Foundation/Rosetta solve, and who is the primary audience?

  pass
  ✓AI named LatticeX-Foundation/Rosetta explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?