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Xreach: Privacy-Preserving Ad Attribution

Measure ad conversions without tracking individual users. XorIDA splits attribution events across independent nodes, enabling aggregate measurement with information-theoretic privacy.

AI / ML COMING SOON XorIDA Powered
Section 01

The Problem

Ad attribution requires tracking individual users across websites and apps. Central surveillance profiles violate privacy regulations (GDPR, CCPA, DMA). Cookie deprecation and ATT are breaking existing measurement infrastructure.

The $600B digital advertising industry depends on knowing which ad exposure led to which conversion. Today this requires linking user identities across publishers and advertisers -- building the surveillance profiles that regulators are now prohibiting.

The measurement gap is widening. Third-party cookie deprecation, Apple ATT, and browser privacy features are eliminating cross-site tracking. Advertisers are losing visibility into campaign effectiveness. Privacy-preserving alternatives like Google's Privacy Sandbox still require trusting a single platform.

The Old Way

User Activity Cross-site tracking Central Ad Server Full user profile Individual tracking SURVEILLANCE MODEL GDPR risk Cookie-dep
Section 02

The PRIVATE.ME Solution

Xreach splits conversion events into N shares using XorIDA before they leave the user's device. Independent attribution nodes aggregate shares without ever seeing individual user behavior. Only aggregate campaign metrics are reconstructable.

When a user converts (purchase, signup, app install), the conversion event is split into N shares. Each share goes to an independent attribution node. Nodes perform partial aggregation on their shares. The advertiser reconstructs aggregate metrics from K nodes -- total conversions, ROAS, channel effectiveness -- without any node seeing individual user data.

Regulation-proof by design. No individual user profile is ever constructed. No cross-site identifier exists. GDPR, CCPA, and DMA compliance is achieved through architecture, not policy.

The New Way

Conversion On-device XorIDA Split K-of-N shares No user profile Attr Node 1 Attr Node 2 Attr Node 3 Aggregate Campaign metrics
Section 03

How It Works

Conversion events are split on-device, distributed to independent attribution nodes, aggregated in share-space, and reconstructed into campaign-level metrics. Individual user behavior is never visible to any party.

Event Conversion Split XorIDA K-of-N On-device Node 1 Partial agg Node 2 Partial agg Node 3 Partial agg Reconstruct HMAC verify Aggregate only Metrics ROAS / CPA
Key Security Properties
Individual user events are split before leaving the device -- no attribution node ever sees a complete conversion event. Aggregate metrics (ROAS, CPA, conversion counts) are reconstructable from K-of-N partial aggregations. No user profiles are constructed at any point in the pipeline.
Section 04

Use Cases

Publishing
Cross-Publisher Attribution

Measure campaign effectiveness across publishers without sharing user data between them. Each publisher receives only partial attribution shares.

multi-publisher
E-Commerce
Conversion Measurement

Track ad-to-purchase conversions without cross-site cookies. On-device splitting ensures no third-party surveillance of purchase behavior.

cookie-free
Advertising
Privacy-Compliant Retargeting

Enable interest-based ad targeting using aggregate cohort data. No individual user profiles. GDPR and CCPA compliant by architecture.

regulation-proof
Analytics
Audience Insights

Generate aggregate audience demographics and interests without tracking individuals. Statistical significance from threshold aggregation.

aggregate-only
Section 05

Integration

Quick Start
import { recordConversion, measureAttribution } from '@private.me/adsplit';

// Record a conversion event (split on-device)
const result = await recordConversion({
  campaignId: 'camp_abc123',
  eventType: 'purchase',
  value: 49.99,
}, { n: 3, k: 2 });

// Measure aggregate campaign performance
const metrics = await measureAttribution('camp_abc123');
if (metrics.ok) {
  // metrics.value.conversions — total count
  // metrics.value.roas — return on ad spend
}
recordConversion(event: ConversionEvent, config: { n: number, k: number }): Promise<Result<AttributionShare[]>>
Splits a conversion event into N shares on-device using XorIDA. Each share is HMAC tagged and sent to an independent attribution node. No single node receives enough information to identify the user or reconstruct the individual event.
measureAttribution(campaignId: string): Promise<Result<CampaignMetrics>>
Reconstructs aggregate campaign metrics from K attribution nodes. Returns conversion counts, ROAS, CPA, and channel breakdown. All metrics are aggregate -- no individual user data is included.
Section 06

Security Properties

PropertyMechanismGuarantee
ConfidentialityXorIDA threshold sharingInformation-theoretic
IntegrityHMAC-SHA256 per shareTamper-evident
AvailabilityK-of-N reconstructionFault tolerant
User PrivacyOn-device splittingNo individual profiles
RegulatoryAggregate-only outputGDPR/CCPA compliant
45
Tests
88%
Coverage
5
Modules
2
Dependencies
VERIFIED BY XPROVE

Cryptographic Proof of Correctness

This ACI's computations can be cryptographically verified by xProve — so regulators, auditors, and counterparties can confirm results without re-running the computation or accessing the underlying data.

XPROVE INTEGRATION
Tier 1: HMAC audit trail — always on, ~1.3x overhead.
Tier 2: Commit-and-reveal — anti-equivocation for Beaver triples.
Tier 3: IT-MACs — malicious-security detection between parties.
Tier 4: KKW zero-knowledge proofs — publicly verifiable, ~50 KB, post-quantum.

Read the xProve white paper →
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© 2026 StandardClouds Inc. dba PRIVATE.ME. All rights reserved.

VERIFIABLE WITHOUT CODE EXPOSURE

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XPROVE CRYPTOGRAPHIC PROOF
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Use Cases

🏛️
REGULATORY
FDA / SEC Submissions
Prove algorithm correctness for distributed systems without exposing trade secrets or IP.
Zero IP Exposure
🏦
FINANCIAL
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External auditors verify secure operations without accessing source code or production systems.
FINRA / SOX Compliant
🛡️
DEFENSE
Classified Verification
Security clearance holders verify distributed systems correctness without clearance for source code.
CMMC / NIST Ready
🏢
ENTERPRISE
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No NDA Required