Reach the Right Audience
Without Wasting Budget

Scenario

A premium automotive advertiser has spent $50M across three major publishers (news, sports, entertainment) over 12 months, claiming a combined reach of 45M users. But the advertiser has no idea how much audience overlap exists. Each publisher reports independent reach metrics, masking the fact that 42% of users see the same ad 4+ times. The advertiser wastes $5M on redundant impressions while missing 2.1M net new prospects entirely.

How It Works

Each publisher sends encrypted, pseudonymized audience IDs (deterministically hashed user tokens with no PII) to Placino's clean room. Advertisers import campaign exposure targets. Placino applies cryptographic deduplication using Private Set Intersection to compute overlaps across publishers in encrypted space—no raw audience IDs are exposed. Results return as deduplicated reach metrics (net unique users, frequency distributions per geography) with k-anonymity protections (k=50) and Merkle-chain audit trails proving the computation, enabling budget reallocation without identifying any individual user.

Scenario

A premium publisher claims 38% attribution for a luxury goods advertiser's campaign—but the advertiser's last-click model credits only 12% to this publisher. The advertiser suspects the publisher overstates value and is threatening to reallocate $15M budget. The publisher cannot share raw conversion event logs (privacy risk), and the advertiser cannot share purchase transaction IDs directly (security risk). Without deterministic cross-domain matching, both operate on incomplete information.

How It Works

Publisher sends encrypted conversion events (click timestamp, user token, ad creative served) to Placino. Advertiser sends encrypted purchase records (transaction timestamp, purchase value, pseudonymized user ID). Placino applies deterministic matching using cryptographic matching protocols with envelope encryption (AES-256-GCM) and time-window matching to attribute purchases to ad exposures in encrypted space. Results return as aggregated attribution models (path analysis, contribution per touchpoint, ROAS by campaign) with k-anonymity thresholds (k=100) and differential privacy noise (epsilon=1.0), proving true media contribution without exposing user identities or transaction details.

Scenario

A lifestyle publisher and a tech publisher have complementary audiences (women 25-40 interested in wellness + men 30-50 interested in gadgets)—but their recommendation engines see only within-site behavior. Tech enthusiasts on the lifestyle site (e.g., smart home + health tracking) get poor tech recommendations. Lifestyle readers on the tech site miss wellness content. Each publisher loses engagement and ad inventory value because their models are incomplete.

How It Works

Each publisher encrypts user interest vectors (hashed user tokens, browsing category tags, content engagement signals, time-spent metrics) but NOT personal identities. Placino applies federated collaborative filtering using Private Set Intersection to identify cross-site interest patterns in encrypted space. Each publisher retrieves anonymized enriched interest vectors (e.g., "tech+wellness cluster") without learning raw user identities or seeing other publisher's users. Recommendation models update to include cross-site preferences, boosting relevance and engagement.

Scenario

A premium publisher has created exclusive audience segments (high-intent luxury buyers, tech adopters, decision-makers) but lacks proof of authenticity. Programmatic buyers are skeptical: is this genuinely a high-intent luxury segment or just a repackaging of generic inventory? Without transparent segment validation, buyers default to open exchange pricing (CPM=$8)—leaving $18M in premium revenue on the table. The publisher cannot share raw user data to prove segment quality (privacy risk), and buyers cannot inspect raw audience lists (security risk).

How It Works

Publisher defines audience cohorts based on verified behaviors (site engagement time, conversion events, purchase history buckets) and loads cohort statistics into Placino (encrypted segment size, historical CTR, conversion rate by geography) using envelope encryption (AES-256-GCM). Buyers send test queries asking for aggregate statistics (e.g., "segment size for luxury segment in NYC", "historical conversion rate"). Placino returns cryptographically verified aggregates with differential privacy noise (epsilon=1.0) and k-anonymity (k=100), proving segment quality through transparent statistics without exposing any individual user. Buyers gain confidence and transact at premium CPM ($20).

Scenario

A streaming service loses 12% of its subscriber base monthly despite strong content metrics (high viewing time, low cancellation intent signals). Meanwhile, a telco partner observes payment friction patterns—declined cards, payment method failures, billing inquiry spikes—that precede cancellations. Each company has partial visibility: the streamer sees engagement, the telco sees payment behavior. Neither has the full picture needed to predict true churn risk. Combined, they could intercept 38% more at-risk subscribers before cancellation—but neither can share raw data (subscriber records are commercially sensitive, billing records are PCI-regulated).

How It Works

Streaming service encrypts engagement features (weekly viewing hours, content category preferences, account age, churn risk scores from its model) and sends to Placino. Telco encrypts billing features (payment failure count, billing inquiry frequency, account status flags) and sends to Placino. Placino trains a federated ML model using encrypted computation: gradient updates are computed locally at each party, then aggregated in Placino with differential privacy noise (epsilon=0.5 per round) to prevent feature leakage. The trained model is deployed to both parties. Each uses the combined model to score at-risk subscribers. Prediction accuracy lifts 38% because the model sees both engagement and payment signals.</description>