# Building Standards for Web3 Grants: Privacy, Transparency, and Lessons Learnt

I work for DAOstar.org (MetaGov), where we specced out grants metadata for the Web3 Ecosystem called DAOIP-5 and led efforts for adoption to enable better grants ecosystem analysis. This journey has taught us a lot about the complexities of standardizing grant data across the decentralized ecosystem.

### The Current State of Grant Data

Essentially, Grant's Metadata has the following components:

- Grant System
- Grant Pool
- Projects
- Applications

Grant Pools are created by Grant Systems, which consist of Applications from Projects. When it comes to running a Grants pool, grant systems collect and store the data in different channels.

### The Data Standardization Challenge

A few problems we faced when collecting and standardizing the data were:

1. It's hard to gather all of this grant data in one place - sometimes applications are managed separately, and projects applying are not uniquely identified.
2. Grants Payment Data is managed separately.
4. Use of different platforms, ranging from custom-built solutions to Airtable, which have different data storage formats.
5. Project Data is appended, not modified. Applications data varies according to grant pool theme.
7. Some grant systems don't report their grant data transparently and most of the required fields in the standard are not fulfilled.

### The Privacy Dilemma

Not all Grant Data can be public. This raises an important question: **Can we identify genuine needs of privacy in the Grants Ecosystem?**

At DAOstar.org, OpenGrants aims to provide comprehensive Web3 grant ecosystem analysis, enabling data-driven decision-making throughout the grant assessment process. But we've discovered that implementing shared or open-source grant administration systems comes with significant security and data privacy considerations.

Organizations like Stellar, Optimism, Arbitrum and Gitcoin champion open data advocacy, courageously making their grant data public despite inherent risks. Their transparency is admirable and demonstrates their ability to manage and defend against potential risks.

However, many organizations face significant barriers to public disclosure. Risks such as:
- **Anchoring bias** in funding decisions
- **Political pressures** from stakeholders
- **Competitive disadvantages** from early disclosure
- **Privacy concerns** for sensitive projects

These factors prevent 40-60% of grant funding organizations from making all their data public.

To provide meaningful grant ecosystem analysis, we cannot rely solely on partial public data. We need a framework that accommodates both transparent and privacy-conscious organizations.

#### A Framework for Privacy-Conscious Grant Data

Grant funding data fundamentally consists of:
- **Who** funded (Grantor)
- **Whom** they funded (Grantee)
- **How much** was funded (Amount)
- **When** it was funded (Timestamp)
- **What** category it falls under (loosely defined) - It sometimes may be Domain, Open Source, Public/Private Company, Defi/DAO/NFT/DeSci

### Privacy Configuration Matrix

A flexible privacy framework with multiple levels:

| Privacy Level | Grantor Identity | Grantee Identity | Funding Amount | Timestamp | Category | Use Case |
|--------------|------------------|------------------|----------------|-----------|----------|----------|
| **Level 0** | ✅ | ✅ | ✅ | ✅ | ✅ | Full transparency (current standard) |
| **Level 1** | 🛡️ | ✅ | ✅ | ✅ | ✅ | Anonymous funders |
| **Level 2** | ✅ | 🛡️ | ✅ | ✅ | ✅ | Protected recipients |
| **Level 3** | ✅ | ✅ | 🛡️ | ✅ | ✅ | Confidential amounts |
| **Level 4** | 🛡️ | 🛡️ | ✅ | ✅ | ✅ | Anonymous parties |
| **Level 5** | 🛡️ | 🛡️ | 🛡️ | ✅ | ✅ | Temporal patterns only |
| **Level 6** | 🛡️ | 🛡️ | 🛡️ | 🛡️ | ✅ | Category insights only |

**Key:**
- ✅ = Public/Visible
- 🛡️ = Private/Encrypted

By computing across all privacy levels, we can provide ecosystem-wide insights while respecting individual privacy preferences.

#### Example Scenario: Privacy Tech Trend Detection
- **January 2024**: Privacy tech grants = 5% of ecosystem
- **February 2024**: Major exchange announces privacy features
- **March 2024**: Privacy tech grants surge to 15% of ecosystem

#### Stakeholder Benefits Scenario:
- **Founders**: "Privacy tech is gaining traction; optimal timing for applications"
- **Investors**: "Market shifting toward privacy; time to adjust investment thesis"
- **Researchers**: "Industry proactively responding to regulatory pressures"
- **Policymakers**: "Self-regulation emerging; heavy intervention may be unnecessary"

### The Reality of Adoption

The main sources of organizational resistance to adopting standardized frameworks or shared infrastructure come down to these key factors:

- It doesn't make them money, it's not a priority.
- The cost of adoption is a bit high, time-wise and resource-wise.
- It's a nice-to-have, not a need.
- Aren't willing to be fully transparent for various reasons.

### Thoughts on moving forward

The path to standardized grant data isn't just about technical specifications; it's about creating frameworks and building infrastructure that respect privacy while enabling the transparency and insights our ecosystem needs to thrive. By acknowledging these challenges and building solutions that work for both transparent and privacy-conscious organizations, we can create a more robust and inclusive grants ecosystem.

The future of Web3 grant data lies not in forcing transparency, but in creating systems flexible enough to accommodate different privacy needs while still providing valuable ecosystem insights.

---
### Resources:

- [DAOIP-5 Spec](https://github.com/metagov/daostar/blob/main/DAOIPs/daoip-5.md)