Secure Data Collaboration Demo

Use consoles representing different insurers inputting sensitive claims data. Witness a central screen calculate an aggregated fraud risk score using ZKPs, without raw data ever leaving its origin or being revealed.

Privacy-Preserving Analytics Demo

See how an insurer runs an AI underwriting model on encrypted customer health data e.g., from wearables using homomorphic encryption or secure MPC, obtaining a risk score without decrypting sensitive details.

Verifiable Credentials (ZKP Aspect)

Observe how a portal verifies a "Verified Business Owner" DID credential using ZKPs – confirming its validity via digital signature without accessing the underlying issuance documentation.

ZKP for Regulatory Reporting Demo

Simulate an insurer generating a regulatory report e.g. solvency where sensitive portfolio details are proven compliant using ZKPs, avoiding granular data disclosure to the regulator.

• Masterclasses: Gain deep understanding with sessions like "Understanding Zero-Knowledge Proofs and Their Game-Changing Potential for Financial Services Privacy" led by academic experts or ZKP company CTOs.

• Debates: Engage with provocative discussions: "This House Believes: Privacy Enhancing Technologies are Mature Enough for Widespread Adoption in Insurance Analytics" featuring Chief Privacy Officers, Data Scientists, Regulators, and ZKP vendors.

• Technical Deep Dives: Explore implementation specifics in sessions like "Implementing ZK-SNARKs vs ZK-STARKs for Secure Insurance Data Pooling" presented by lead cryptographers.

Strategic Negotiations: Forge strategic partnerships that can significantly enhance the capabilities of all parties involved. For instance, representatives from various health insurers' innovation and data teams come together to collaborate with a solutions architect and a legal advisor from a cutting-edge zero-knowledge proof (ZKP) technology provider.

They engage in comprehensive discussions to investigate the potential for pooling anonymized data, specifically aimed at improving rare disease risk modeling through the utilization of ZKPs. This collaborative effort not only aims to advance healthcare research but also seeks to address crucial privacy concerns that arise when dealing with sensitive patient information. By pooling their collective expertise, these stakeholders hope to create innovative solutions that benefit both the industry and the patients they serve.

See ZKP Converge: Witness ZKP enabling secure data flows in labs like:

• Lab 1 Privacy Pooling: ZKP is the core technology enabling secure aggregation of sensitive data from different sources (via DDE) before AI analysis (EAI).

• Lab 2 Embedded UBI: A simulated ZKP control allows users to manage privacy preferences for sharing sensitive driving data used by EAI for scoring within a DDE-powered embedded offer.

Focused Discussions: Tackle the regulatory implications and standardization needs:

• From a regulator's view, does proving a property with ZKP constitute sufficient evidence versus seeing raw data?

• What are the systemic risks if ZKP implementations have flaws?



• How can we standardize ZKP approaches for interoperability in regulated reporting?

• Enables Secure Data Collaboration: Pool sensitive data for richer insights: fraud, risk without exposure.

• Enhances Customer Trust: Process personal data for analytics while preserving privacy.

• Unlocks New Data Sources: Utilize sensitive third-party data e.g., health securely.

• Streamlines Verification: Verify credentials and compliance efficiently and privately.

• Future-Proofs Data Strategy: Navigate evolving privacy regulations effectively.