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# Privacy scoring modelling > Web3privacy now analytical [platform](https://github.com/Msiusko/web3privacy/tree/main/Web3privacynowplatform)
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_Currently we are surveying developers, security engineers and researchers to find out bottle necks & potential room for growth within privacy features assessment by techies._
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## Core user group (techies definition):
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- junior or general devs with under 1-year web3 coding experience
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- poor security auditing skills
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- general interest in privacy tech
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## Why this group
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- general public within the devs landscape is the most vulnerable audience compared to mature decs with connections & experience
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- generalisation helps to make universal tooling & secure research from the niche approaches
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## General scoring will consist of
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- audit check up (manual)
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- scoring features
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- dev toolkit
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- DYOR expanded track (subjective observations)
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## Quick take from market assessment
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- devs lack the basics of security audits (while auditing companies or security engineers create piles of written that require lots of dev hours to read & more to understand)
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- case studies could help to "visualize" the assessment journey
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- DB of the malicious practices & critical bugs within the privacy landscape will come in handy for practical devs
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**Hypothesis**: can the market create a standard of cross-project privacy features check-up increasing general privacy stack levelling?
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**Crucial scoring context**: time on research x potential amount of privacy anti-features detection. Meaning that scoring could be scalable if more people will use it under the economy, general business & without expert knowledge level.
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# MVP for non-techies expanded to techies
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**Sandbox: DeFi category that has been analyzed**
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