Skip to content

AP3 - Use Cases

AP3 enables privacy-preserving collaboration across industries. This page explores real-world scenarios where AP3 creates significant value by allowing organizations to collaborate on sensitive data without compromising confidentiality.

Finance & Banking

Joint Credit Risk Assessment

Challenge: Banks need to assess credit risk for customers who may have relationships with multiple financial institutions. However, sharing customer data directly violates privacy regulations and exposes competitive intelligence about customer portfolios.

AP3 enables bank's AI agents to compute joint credit-risk scores using Secure Function Evaluation (SFE) without revealing individual customer data, transaction histories, or proprietary risk models.

Example Scenario:

Bank A and Bank B want to assess the creditworthiness of a shared customer. Using AP3, agents jointly compute a risk score that combines:

  • Bank A's transaction history (encrypted)
  • Bank B's credit utilization data (encrypted)
  • Both banks' proprietary risk models (hidden)

The result is a comprehensive risk score without either bank seeing the other's data or algorithms.

Fraud Pattern Detection

Challenge: Financial institutions need to identify fraudulent accounts and patterns across organizations, but sharing fraud databases would expose customer information and proprietary fraud detection strategies.

AP3 enables bank's AI agents to identify overlapping fraud patterns between their databases without revealing non-matching entries or the full contents of their fraud lists.

Example Scenario:

Bank X has flagged 1,000 suspicious accounts. Bank Y maintains a fraud database of 5,000 known fraudulent accounts. Using PSI, they discover 50 overlapping accounts, enabling both banks to:

  • Immediately freeze the matched accounts
  • Share fraud indicators (not raw data) to improve detection
  • Build better fraud models without exposing their databases

Supply Chain & Logistics

Production-Demand Optimization

Challenge: Suppliers need to align production schedules with retailer demand forecasts, but neither party wants to reveal their cost structures, profit margins, or strategic inventory levels.

AP3 enables Secure Function Evaluation for linear programming optimization, allowing suppliers and retailers to find optimal production and ordering schedules while keeping costs, margins, and inventory levels private.

Example Scenario:

A manufacturer needs to optimize production for the next quarter based on a retailer's demand forecast. Using AP3:

  • The retailer provides encrypted demand projections
  • The manufacturer provides encrypted production capacity and costs
  • They jointly compute an optimal production schedule
  • Neither party sees the other's proprietary cost or pricing data

Supplier Verification & Quality Assurance

Challenge: Manufacturers need to verify supplier quality metrics against their standards, but suppliers don't want to reveal proprietary product formulations, and manufacturers want to keep evaluation criteria confidential.

AP3's Secure Dot Product computation allows manufacturers to evaluate supplier products against quality thresholds without revealing either party's private values.

Example Scenario:

A manufacturer evaluates a supplier's detergent product. The supplier has private quality metrics (detergency: 8.5, foaming: 7.2, pH: 6.8). The manufacturer has private evaluation weights (detergency: 0.4, foaming: 0.3, pH: 0.3) and threshold (7.0).

  • Compute the quality score
  • Compare the quality score to the threshold
  • Neither party sees the other's values

Human Resources & Hiring

Secure Background Checks

Challenge: Companies need to verify candidates against sanctions lists and blacklists held by other organizations, but sharing candidate information or blacklist contents violates privacy laws and exposes sensitive HR data.

AP3's Private Set Intersection (PSI) protocol enables companies to check if a candidate appears in another organization's sanctions database without revealing the candidate's identity to the database owner or the blacklist contents to the hiring company.

Example Scenario:

Company A is hiring a delivery driver and wants to check against Company B's blacklist of 10,000 flagged individuals. Using PSI:

  • Company A provides encrypted candidate identifiers
  • Company B provides encrypted blacklist entries
  • The protocol returns only matches (if any)
  • Company B never learns who Company A is checking
  • Company A never sees Company B's full blacklist

Fast-Moving Consumer Goods (FMCG)

Secure Product Compatibility Evaluation

Challenge: Manufacturers need to evaluate supplier products against quality standards, but both parties want to keep their proprietary metrics, evaluation criteria, and product formulations confidential.

AP3's Secure Dot Product computation enables manufacturers to evaluate product compatibility by computing weighted quality scores without revealing either party's private values.

Example Scenario:

A manufacturer evaluates a supplier's cleaning product

  • The supplier provides encrypted quality metrics (detergency, foaming, pH levels)
  • The manufacturer provides encrypted evaluation weights and thresholds.
  • They computes the compatibility score and comparison result without revealing either party's values.

Delivery & Quick Commerce

Cross-Company Delivery Partner Verification

Challenge: Delivery companies need to verify new delivery partners against blacklists maintained by other companies, but sharing blacklist contents or candidate information violates privacy and exposes competitive intelligence.

AP3's Private Set Intersection (PSI) protocol allows delivery companies to check if a delivery person appears in another company's blacklist without revealing the candidate's identity or the blacklist contents.

Example Scenario:

Delivery Company A wants to onboard a new delivery person and checks against Delivery Company B's blacklist of flagged individuals. Using PSI:

  • Company A provides encrypted candidate identifiers
  • Company B provides encrypted blacklist entries
  • The protocol identifies matches (if any) without revealing non-matches
  • Company B never learns who Company A is checking
  • Company A never sees Company B's full blacklist