AP3 - Commitments

Commitments allow agents to commit to providing data of a specific structure, format, and count without revealing the actual data content. This enables informed decision-making about privacy-preserving computations while maintaining complete data confidentiality.

The Data Disclosure Dilemma

Companies need to make informed decisions about whether to engage in privacy-preserving computations, but they don't want to reveal their actual data. Traditional approaches either:

• Reveal too much: Share actual data samples (privacy violation)
• Reveal too little: No information for decision-making (blind engagement)

Solution: Commitments

Commitments provide a middle ground by revealing:

• Data structure and format
• Entry count and estimated size
• Data freshness and coverage
• Industry and purpose

Note

No actual data content, individual entries, sensitive information are revealed in commitements.

Data Structures and Formats

Supported Data Structures

Structure	Purpose	Typical Fields
BLACKLIST	Fraud prevention	person_id, name, phone, reason, date_added
CUSTOMER_LIST	Customer analytics	customer_id, name, email, segment, signup_date
TRANSACTION_LOG	Financial analysis	transaction_id, amount, date, merchant, type
PRODUCT_CATALOG	Product matching	product_id, name, category, price, availability
SUPPLY_CHAIN_DATA	Supply optimization	supplier_id, product, cost, delivery_time, quality

Data Formats

Format	Description	Use Case
STRUCTURED	JSON, CSV, database records	Blacklists, customer lists
UNSTRUCTURED	Free text, documents	Reports, descriptions
SEMI_STRUCTURED	Mixed format	Logs, mixed data sources
BINARY	Binary data, images	Media, encoded data

How it Works

Let's take an example of XYZ Agent who wants to commit to a blacklist of 10000 entries with 5 fields and ABC agent who wants to find suitable partners for a blacklist of 5000 entries with 5 fields.

1. Creating Commitments - XYZ agent

from ap3.commitments import (
    CommitmentMetadata,
    DataStructure,
    DataFormat,
    DataFreshness,
    Industry,
)

xyz_data_commitment = CommitmentMetadata(
    commitment_id="xyz_data_commitment_v1",
    data_structure=DataStructure.BLACKLIST.value,
    data_format=DataFormat.STRUCTURED.value,
    entry_count=10000,
    field_count=5,
    estimated_size_mb=4.8,
    last_updated="2025-01-20T10:00:00Z",
    data_freshness=DataFreshness.DAILY.value,
    industry=Industry.FOOD_DELIVERY.value,
)

2. Sign the commitment - XYZ agent

Agent signs the commitment to prove the commitment is valid and not tampered with.

signed_commitment = xyz_data_commitment._sign_commitment()
print(signed_commitment)

{
  "commitment_id": "xyz_data_commitment_v1",
  "signature": "xyz_signature_12345",
}

3. Public Metadata - XYZ agent

The commitment creates public metadata that can be safely shared:

{
  "commitment_id": "xyz_data_commitment_v1",
  "data_structure": "blacklist",
  "data_format": "structured",
  "entry_count": 10000,
  "field_count": 5,
  "estimated_size_mb": 4.8,
  "last_updated": "2025-01-20T10:00:00Z",
  "data_freshness": "daily",
  "industry": "food_delivery",
  "signature": "xyz_signature_12345",
}

4. Discovery - ABC agent

Discover and evaluate commitments without seeing their own data:

# Searches for suitable partners
suitable_partners = discovery_service.find_suitable_partners(
    requester_agent_id="ABC_Agent",
    data_structure=DataStructure.BLACKLIST,
    min_entry_count=5000,
    industry="food_delivery"
)

# Evaluate each partner
for partner in suitable_partners:
    evaluation = evaluate_partner(partner)
    print(f"Agent: {partner['agent_id']}")
    print(f"Entry Count: {partner['entry_count']:,}")
    print(f"Suitability Score: {partner['suitability_score']:.1f}/100")

5. Evaluation and Scoring - ABC agent

def evaluate_partner(partner_metadata):
    score = 0.0

    # Entry count score
    if partner_metadata.entry_count >= min_required:
        score += 50.0

    # Data freshness score
    freshness_scores = {
        "real_time": 30.0,
        "daily": 25.0,
        "weekly": 15.0
    }
    score += freshness_scores.get(partner_metadata.data_freshness, 0.0)

    # Size efficiency score
    if partner_metadata.estimated_size_mb <= 100:
        score += 15.0

    return min(score, 100.0)

Roadmap

• Commitments can evolve into a trusted regitry of commitments that can be used to find suitable partners for privacy-preserving computations.
• Automated scoring and evaluation of commitments can be added to the protocol.
• Enforcement and reward system can be added to the protocol to encourage agents to commit to more suitable commitments.