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Privacy Scaling comes to Starknet! Port existing privacy scaling projects from Ethereum to Starkent for cost efficiency by verifying Groth16 proofs on Starknet


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Project Description

PrivacyScalingSN: Enhancing Privacy and Efficiency on Starknet

Project Overview: PrivacyScalingSN is an innovative initiative that brings advanced privacy scaling solutions to the Starknet ecosystem. By leveraging the power of Groth16 proofs, we aim to port existing privacy projects from Ethereum to Starknet, achieving greater cost efficiency and scalability.

Core Features:

  1. Groth16 Proof Verification with Garaga: We utilize the Garaga project to seamlessly verify Groth16 proofs on Starknet. This integration ensures robust and efficient verification processes, laying the foundation for secure and private transactions on the network.

  2. Porting Anon Aadhar to Starknet: PrivacyScalingSN is proud to port the PSE’s Anon Aadhar project to Starknet. This feature allows users, particularly from India, to prove their identity without revealing sensitive information from their Aadhar Card. By providing proof of identity in a privacy-preserving manner, we enhance user confidentiality and trust.


  • Cost Efficiency: Transitioning privacy scaling projects from Ethereum to Starknet significantly reduces costs, making advanced privacy solutions more accessible and affordable.

  • Enhanced Privacy: Users can leverage sophisticated cryptographic proofs to protect their personal information, ensuring privacy without compromising on security.

  • Scalability: Starknet’s high-performance environment allows for seamless scaling of privacy-centric applications, supporting a growing user base without degradation in performance.

PrivacyScalingSN is committed to pushing the boundaries of privacy and scalability on Starknet, providing users with cutting-edge tools to safeguard their digital identities and transactions.

How it's Made

How PrivacyScalingSN is Made

Development Process:

  1. Utilizing the Garaga Project:

    • Integration: We begin by integrating the Garaga project to verify Groth16 proofs on Starknet. Given the time constraints of a hackathon, this step focuses on ensuring basic functionality.
    • Basic Setup: The initial setup involves connecting Garaga with our Starknet environment to enable Groth16 proof verification, ensuring it runs without major issues.
  2. Porting Anon Aadhar:

    • Analysis and Adaptation: We quickly analyze the core components of the Anon Aadhar project on Ethereum. Essential smart contracts and functions are adapted to work within Starknet's framework.
    • Implementation: Basic implementation of the Anon Aadhar system is carried out on Starknet, ensuring that it can demonstrate identity proof without revealing sensitive Aadhar data.
    • Integration: The adapted Anon Aadhar system is integrated with the Garaga-based verification module, ensuring the basic functionality of identity proof verification on Starknet.
  3. Optimization and Testing:

    • Performance Tuning: Basic performance tuning is done to ensure that the proof verification process is functional within the hackathon timeframe.
    • Testing: Limited testing is conducted to validate the core functionalities, focusing on ensuring that the system works end-to-end without critical bugs.
  4. Deployment:

    • Deployment: The project is deployed on Starknet as a proof of concept. Emphasis is on showcasing the potential of porting privacy scaling projects from Ethereum to Starknet.
  5. Presentation Preparation:

    • Demo Creation: A simple demo is prepared to showcase the key features and functionalities of PrivacyScalingSN during the hackathon presentation.
    • Documentation: Basic documentation is created to explain the setup, usage, and potential of the project, highlighting its ability to verify identity proofs while maintaining privacy.

PrivacyScalingSN, created as a hackathon proof of concept, demonstrates the feasibility and potential of bringing advanced privacy scaling solutions to Starknet. The focus is on showcasing core functionalities and integrating key components to provide a glimpse of what full-scale implementation could achieve.

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