Confidential machine learning

1) Essence and goals

Privacy-preserving MLs are approaches that allow you to train and use models, minimizing access to source data and limiting leaks about specific users. For iGaming, this is particularly important due to PII/financial data, regulatory (KYC/AML, RG), partner integrations (game providers, PSPs), as well as cross-border requirements.

• Reduce the risk of leaks and regulatory penalties.
• Enable collaborative learning across brands/markets without sharing raw data.
• Make the "privacy price" in ML (metrics, SLO) explainable and verifiable.

2) Threat model in ML

Model Inversion-Attempts to restore the original examples/attributes from the model.
Membership Inference: determining whether the recording was involved in training.
Data Leakage in pipeline: logs/fichesters, temporary files, snapshots.
Proxy/Linkage attacks: gluing anonymized data to external sources.
Insider/Partner risk: redundant privileges in accesses/logs.

3) PPMl tools and approaches

3. 1 Differential Privacy (DP)

The idea: adding controlled noise to ensure that a single subject's contribution is "indistinguishable."

Where to apply: aggregations, gradients in learning (DP-SGD), reports/dashboards, publishing statistics.

Parameters: ε (epsilon) - "privacy budget," δ - the likelihood of "failure."

Bargaining is appropriate: more noise → more privacy, lower accuracy; Plan budget accounting for the model lifecycle.

3. 2 Federated Learning (FL)

The idea: the model goes to the data, not the other way around; gradients/weights are aggregated rather than raw records.
Options: cross-device (many customers, weak nodes), cross-silo (several reliable organizations/brands).
Security enhancers: Secure Aggregation, DP over FL, resistance to low-quality/malicious clients (byzantine-robust).

3. 3 Secure Computing

MPC (Secure Multi-Party Computation) - joint computing without opening inputs to each other.
HE (Homomorphic Encryption): calculations over encrypted data; expensive but useful for point tasks (scoring/inference).
TEE/Confidential Computing: trusted executable environments (enclave), code and data isolation at the HW level.

3. 4 Optional

Knowledge-without-disclosure (ZKP): prove correctness without data disclosure (niche cases).
Pseudonymization/anonymization: before training; re-identification risk check.
Private Set Intersection (PSI): intersection of sets (fraud/sanction lists) without revealing the entire set.

4) Architecture patterns for iGaming

4. 1 Private feature lines

PII is separate from gaming telemetry events; keys - via tokenization/salted hashing.
Fichestor with access levels: raw (Restricted), derived (Confidential), aggregates (Internal).
DP aggregations for reporting and research; ε quotas by domain (marketing/risk/RG).

4. 2 Collaborative learning

Cross-brand FL: general anti-fraud/RG scoring for the holding → local gradients, central aggregation with Secure Agg.
MPC inference with PSP: scoring payment risk on the PSP and operator side without exchanging raw features.

4. 3 Private inference

Scoring requests for VIP/payouts go through the TEE service or the selected submodel's HE assessment.
Caching only aggregated results; a ban on serializing a "raw" fiche cast.

5) Processes and Governance

5. 1 "Minimal data" policy

Clear purpose of processing, list of permissible features, shelf life.
PII separately, access - RBAC/ABAC, Just-in-Time, logging.

5. 2 RACI for PPMl

CDO/DPO - privacy policy, DPIA/DEIA, coordination of ε budgets.
ML Lead/Data Owner - selection of techniques (DP/FL/MPC/TEE), quality validation.
Security/Platform - keys/secrets, confidential environments, audit.
Stewards - catalog/classification, data statements, set passports.

5. 3 Pre-release checks

DPIA/ethical impact assessment.
Fairness + group calibration (no hidden proxies).
Privacy-тесты: membership inference, gradient leakage, re-identification.

6) Privacy metrics and SLOs

ε -budget usage: cumulative consumption by models/doms.
Re-identification risk: probability of de-anonymization (simulation/attack-tests).
Attack AUC↓: The success of membership/inversion attacks must be ≈ chance.
Leakage rate: logging/snapshots incidents with PII = 0.
Coverage:% of models with DP/FL/MPC/TEE where required.
Latency/Cost SLO: private computation overhead <target threshold for production paths.

7) iGaming Domain Practice

7. 1 KYC/AML

PSI + MPC for sanction list/PEP matchup without full set disclosure.
DP aggregations for risk pattern reporting.

7. 2 Responsible Gaming (RG)

FL between market brands for a common risk detector; strict overrides by self-exclusion.
DP publications of RG studies to exclude deanonymization of cases.

7. 3 Antifraud/Payments

TEE for scoring high-risk payments; MPC chargeback probability score with PSP.
Audit of inference logs: without feature dumps and PII in tracks.

7. 4 Personalization/CRM

DP aggregates for segmentation "narrow" features (frequency, genres, sessions) without a detailed player trajectory.
Off-device FL for look-alike models by grainy features.

8) Privacy testing and verification

Membership Inference Challenge: A public (internal) competitive test against a model.

Gradient/Activation Leakage Tests

K- anonimnost/ℓ -diversity/t-closeness: formal criteria for impersonal samples.
Canary records: artificial records for detecting leaks in the log/model.

9) MLOps: from development to production

Policy-as-Code: linter feature/contracts with PII labels; CI blocks unauthorized features.
DP learning in contours: ε control in CI, budget depreciation report.
Secrets/KMS: keys for MPC/HE/TEE, rotation and dual control.
Observation without leaks: masking in logs, sampling, PII disabling in traces.
Model Registry: data version, ε/ δ, privacy technique, review date, owner.

10) Templates (ready to use)

10. 1 Private model card (fragment)

Task/Impact: (RG/AML/Antifraud/CRM)

Privacy technique: (DP ε =?, FL, MPC/TEE/HE)

Data/features: (classes, PII tags, sources)

Quality metrics: AUC/PR, calibration

Privacy metrics: ε -usage, Attack AUC, re-id risk

Fairness section: Target EO/EO + Calibration

Constraints: where model does not apply

Environment: confidential nodes/keys/logging policies

10. 2 DP Policy (thumbnail)

Budgets by Domain - Marketing ≤ X, Risk ≤ Y

ε Accounting - Increment Reporting During Training/Analytics

Minimum quality thresholds: so as not to "noise" to zero

Exceptions: DPO/CDO decision with justification record

10. 3 Private Release Checklist

• DPIA/ethics passed, owners appointed
• PII separated, features allowed by policy
• DP/FL/TEE/MPC configured and tested
• Attack-suite: membership/inversion ≈ random
• Logs/trails without PII, retension set
• Documents: model card + privacy appendix

11) Implementation Roadmap

0-30 days (MVP)

1. PII-tagged feature catalog; PII prohibition in logs/traces.
2. Include DP for key aggregates and research reports.
3. Run basic attack tests (membership/inversion) and reporting.
4. Model cards with privacy parameters and owners.

30-90 days

1. Pilot FL (cross-silo) for one task (for example, RG or anti-fraud).
2. Confidential environments (TEE) for scoring payments/VIP.
3. Policy-as-Code: feature linter + privacy CI locks.
4. Set up ε accounting and privacy-SLO dashboards.

3-6 months

1. MPC/PSI to match sanctions/fraud lists with PSP/partners.
2. HE/TEE for private inference point scenarios.
3. Regular privacy-pentest ML, canary-records, post-morThemes.
4. DP/FL coverage on all high-impact models; annual audit.

12) Anti-patterns

"Anonymization" without re-identification risk assessment.
FL without Secure Aggregation and without DP - gradients can flow.
Inference/fichestore logs with PII.
Lack of accounting for ε and public (internal) privacy reports.
Zero plan in case of incident (no playbook and communications).

13) Playbook Incident (Brief)

1. Detection: signal from attack-suite/monitoring/complaint.
2. Stabilization: stop the release/model/campaign, isolate the environment.
3. Rating: scale/data types/time, who is affected.
4. Communication: players/partners/regulator (where required).
5. Mitigation: pipeline patches, revoke keys, strengthen DP/policies.
6. Lessons: Update policies, tests, train teams.

14) Connection with neighboring practices

Data Governance, Data Origin and Path, Data Ethics, Reducing Bias, DSAR/Privacy, Model Monitoring, Data Drift - the basis for managed, responsible and verifiable privacy.

Total

Confidential ML is an engineering and management discipline: the right techniques (DP/FL/MPC/TEE), strict processes (Policy-as-Code, ε-accounting, attack tests), conscious compromises between accuracy and privacy, and constant monitoring. In iGaming, those who can scale analytics and AI win without revealing too much and maintaining the trust of players, partners and regulators.