Update Scoringmodel_techies.md
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Mykola Siusko
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| Number of nodes/servers/ -> the larger the footprint the best privacy | + |
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# Storage
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| Scoring | Non-web3 person assesment | Web3, but non-tech assesment |
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| ------------- | ------------- | ------------- |
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| e2e encrypted LOCAL storage | - | + |
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| What user information is stored? (username, IP address, last connection, wallets associate, etc) | - | + |
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| Where is it stored? (centralized server, certain jurisdictions, on-chain, in browser/local cache) | - | + |
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| Scoring | Techie |
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| ------------- | ------------- |
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| e2e encrypted LOCAL storage | + |
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| What user information is stored? (username, IP address, last connection, wallets associate, etc) | + |
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| Where is it stored? (centralized server, certain jurisdictions, on-chain, in browser/local cache) | + |
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# Data aggregation
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| no email or tel nr for signup | + | + |
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| control over personal data | - | - |
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| does not implement KYC or AML | + | + |
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| Metadata privacy / Minimal to no metadata capture | - | - |
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| Scoring | Techie |
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| ------------- | ------------- |
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| no email or tel nr for signup | + |
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| does not implement KYC or AML | + |
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# Traction
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| Amount of transactions | + | + |
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| number of people using it | + | + |
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| is it famous | + | + |
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| Latency | - | - |
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| Time of test and battle-tested code - (e.g. how BSC had passed the stress time of withdrawals with FTX drama or crypto schemes such as ECDSA with more than 2-3 decades alive) | - | - |
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| Cost | - | + |
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| Scoring | Techie |
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| ------------- | ------------- |
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| Amount of transactions | + |
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| number of people using it | + |
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| is it famous | + |
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# Governance
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| DAO structure (if applied) | - | + |
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| Scoring | Techie |
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| ------------- | ------------- |
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| DAO structure (if applied) | + |
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# Privacy execution
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| How is it being transmitted? (encrypted, unencrypted, offuscated, etc) | - | - |
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| Combined those encryption methods effectively (holistic solution) | - | - |
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| Confidentiality of transactions | - | - |
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| the ability to hide transactional data from the public | - | - |
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| strong encryption algorithms | - | - |
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| If the speed in connection is too fast, there most probably no privacy there and rather a direct channel between user - app | - | - |
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| p2p / no central server | - | - |
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| Trustless - No ID required (this is where ZKs are useful) | - | + |
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| Usage of ZK | - | - |
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| Scoring | Techie |
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| ------------- | ------------- |
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| p2p / no central server | + |
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| Trustless - No ID required (this is where ZKs are useful) | + |
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# Product-centric
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| Onboarding steps | + | + |
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| Usability - for end users or in the developer experience if it is a B2B project. | + | - |
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| Scoring | Techie |
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| ------------- | ------------- |
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| Onboarding steps | + |
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# Testing
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| Ability to run part of the service and verify for myself | - | - |
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| try to trace a transaction | - | - |
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| There is a way to verify the code I think is running, really is running e.g. attestation service | - | - |
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| Other tooling to verify e.g. block explorers | - | + |
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# Other
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| Scoring | Non-web3 person assesment | Non-tech assesment |
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| ------------- | ------------- | ------------- |
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| Entropy (non-trivial to estimate, different measurements for type of service). Some examples: https://arxiv.org/abs/2211.04259 or https://blog.nymtech.net/an-empirical-study-of-privacy-scalability-and-latency-of-nym-mixnet-ff05320fb62d | - | - |
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| Censorship-resistant (how hard it's for a powerful party to block/censor a given service) | - | - |
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| Precise description of the concrete privacy properties. Privacy is complicated, so if they don't say exactly what they protect, then its likely vapour | - | - |
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| Doesn’t sell your data | - | - |
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| protects against a global passive adversary | - | - |
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| strong secure anonymity tech | - | - |
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| Credibly neutral | + | + |
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| ISO/IEC 29190:2015: https://www.iso.org/standard/45269.html | - | - |
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| Anonymity Assessment – A Universal Tool for Measuring Anonymity of Data Sets Under the GDPR with a Special Focus on Smart Robotics: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3971139 | - | - |
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_Huge thanks everyone who contributed! I make it anon now, but will thank everyone (who would liked to be credited) once a scoring model will be published on GitHub for community evaluation._
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# 2. My personal notes on privacy scoring (they were made before communal survey)
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_Sketches what could be put inside privacy-solutions scoring model_ (note: think of these as questions to experts for a workshop on scoring ideation).
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**Key observations**
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| Topic | Observation |
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| Scoring | Techie |
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| ------------- | ------------- |
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| Broad range of different takes on privacy assesment | Privacy experts have around 50+ tips |
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| Tech-centricity of assesment | Majority of the expert takes are hard to execute by non-tech people (they need info-help!) |
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| Privacy assessment takes enormous time | Time-To privacy-fit - potential for analytical service |
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| Privacy literacy isn't enough | The scoring model demand both "decentralisation", "open-source" & "privacy" topics understanding |
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| Mix of objective & subjective takes | Scoring criteria are different from objective (example: transaction traceability) & subjective (example: backed by a16z crypto) takes |
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| try to trace a transaction | + |
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| Other tooling to verify e.g. block explorers | + |
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**Open-source transparency**
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- **GitHub repos**: # of commits, # stars, date of repo creation.
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# MVP for non-tecies expanded to techies
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**Third-party validation**
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- **Security audits**: yes, no; type of audit; ammount of audits.
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# Sandbox: DeFi category that has been analyzed
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**Community validation**
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- Existing bugs
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- White hackers assessment (like Secret Network TEE bug)
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- Negative Discord, Twitter, other public feedback (product & founder-centric)
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**How to use sandbox?**
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1. Read takeaways.
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2. Give us feedback via general comments in the Community on [Signal](https://chat.web3privacy.info/) or make a Pull request here.
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3. You can always explore 38 DeFi project' assessment [here](https://github.com/web3privacy/web3privacy/blob/main/Web3privacynowplatform/scoringmodel/DeFi%20category%20prototype.md)
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**Team**
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- Market validation
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- GitHub contribution
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- Track record (incl. red flag projects)
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# Scoring model 1.2: validity track
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**Financials**
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- Investments
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- TVL (like Aztec's L2)
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- Donation-based
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- Public treasury
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_Validity track covers GitHub, Product-readiness, Team, Docs, Audit._
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**Liveliness**
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- How active is GitHub activity
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- How active is the community
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- Is there public product traction?
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**Note**: quick assessment helps to decrease privacy dark patterns from obscure language to test-net claiming it has a "state of art privacy".
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**Product-readiness**
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- State of product-readiness
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- MVP-readiness
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- Protocol (test-net/main-net)
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- dApp (release timing, third-party validation like AppStore/Play Store)
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- network-reliability (the state of privacy in Ethereum, Solana, Avalanche etc)
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**Cross-checked data leakage**
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- Complementing privacy stack data leakage (example: phone + dApp; wallet + RPC etc)
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- Third-party data leakage (from the hackers to state agents (think of Iran or North Korean govs))
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**Data aggregation policies**
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_Reference_: https://tosdr.org
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**Centralisation level (incl KYC)**
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Reference: https://kycnot.me/about#scores
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We use % as a simplified way to prototype scoring model (from % to 100%). Later versions will include a mixmodel of %, yes/no assumptions & much complex observations.
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