Duality SecurePlus

Duality SecurePlus is an award-winning privacy-enhancing technology platform built on fully homomorphic encryption (FHE) that enables secure analysis, querying, and machine learning on encrypted data without decryption.

Reviewed by 7wData

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Duality SecurePlus is an award-winning privacy-enhancing technology platform built on fully homomorphic encryption (FHE) that enables secure analysis, querying, and machine learning on encrypted data without decryption. Founded in 2016 and headquartered in Hoboken, New Jersey, Duality Technologies targets regulated industries including financial services, healthcare, insurance, telecommunications, and government sectors where data sensitivity and regulatory compliance are critical. The platform addresses a specific market need: enabling multi-party data collaboration—such as cross-institutional financial crime investigations or statistical analysis across healthcare networks—while keeping sensitive data encrypted throughout computation.

SecurePlus comes as a modular suite: SecurePlus Query enables encrypted database queries across institutions for AML and fraud detection; SecurePlus Statistics supports privacy-preserving statistical analyses on multi-source encrypted datasets; SecurePlus ML runs machine learning training and inference on encrypted data at scale. The technology uses fully homomorphic encryption combined with multi-party computation, offering post-quantum security protections. Deployment is cloud-native via AWS, Google Cloud, Azure, and Oracle Cloud Infrastructure, with SDK and REST API integration options.

While homomorphic encryption delivers strong theoretical privacy guarantees, practical adoption remains limited by implementation complexity, computational overhead (2-5x slowdown in some cases), and market awareness—only 15% of enterprises fully understand privacy-enhancing technologies. Duality has partnerships with Intel, Google Cloud, NVIDIA, DARPA, and the U.S. Army, positioning it as the leading productized FHE solution for real-world data collaboration problems.

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How it works

  1. Fully Homomorphic Encryption

    Performs computations directly on encrypted data without decryption, keeping data encrypted in transit, at computation, at use, and at rest.

  2. SecurePlus Query

    Privacy-enhanced query engine enabling banks and institutions to securely query each other's data for AML and financial crime investigations, receiving answers in seconds without exposing sensitive information.

  3. SecurePlus Statistics

    Enables organizations to apply statistical analyses across encrypted datasets from multiple sources simultaneously, supporting collaborative analytics without decryption.

  4. SecurePlus ML

    Supports machine learning model training and inference (linear regression, logistic regression, XGBoost) on encrypted data at scale via AWS and cloud platforms.

  5. Multi-Party Computation

    Combines homomorphic encryption with secure multi-party computation protocols to enable encrypted collaboration between multiple institutions or data silos.

  6. Post-Quantum Security

    Provides cryptographic protections against quantum computing threats, future-proofing sensitive data collaboration.

  7. High-Throughput Encrypted Queries

    Executes thousands of encrypted queries per second on optimized hardware, supporting production-scale data operations with built-in schema alignment and data transformation.

Strengths and trade-offs

Strengths

  • Only productized fully homomorphic encryption solution for real-world collaborative data analysis; partnerships with Intel, Google Cloud, NVIDIA, and U.S. Army validate technical maturity.
  • Cloud-native architecture on Oracle, AWS, and Google Cloud eliminates on-premise scalability bottlenecks and reduces hardware/energy costs for customers.
  • Addresses specific regulatory compliance needs in finance and healthcare where GDPR and CCPA require data sharing without exposure; enables AML investigation collaboration across borders.

Trade-offs

  • Computational overhead of 2-5x slowdown in encrypted operations; requires specialized expertise and substantial computing resources that limit adoption among smaller organizations.
  • Market education barrier: only 15% of enterprises understand privacy-enhancing technologies; limited brand awareness outside specialist compliance circles delays revenue growth.
  • Complex implementation requiring deep cryptographic expertise; no public pricing, forcing enterprise sales cycles; narrow use-case focus (financial crime, healthcare analytics) limits TAM compared to general-purpose data platforms.

Pricing context

Duality SecurePlus pricing is not publicly disclosed; the company operates on enterprise custom pricing with no standardized tiers, SaaS subscription, or usage-based models visible online. Interested customers must contact sales directly (+1 203-676-3752 or via website). The company has raised $30M in funding (Series B from LG Technology Ventures and Intel Capital as of 2021), suggesting focus on institutional deployments rather than self-serve models.

Cloud deployment via AWS or Oracle likely involves per-compute-hour or per-query fees, but exact structures are not confirmed. No freemium or trial tier is advertised.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.prnewswire.com — SecurePlus Query product capabilities, homomorphic encryption for AML investigations, cross-institutional collaboration without decryption
  2. businessmodelcanvastemplate.com — Company strengths (partnerships with Intel, Google Cloud, NVIDIA), weaknesses (implementation complexity, market education barriers, 15% awareness), opportunities (secure AI market), threats (cybercrime costs, competing PET solutions)
  3. docs.oracle.com — Cloud deployment on Oracle Cloud Infrastructure, use of Intel Ice Lake CPUs, multi-party data collaboration architecture
  4. www.cbinsights.com — Company founding (2016), headquarters (Hoboken, New Jersey), CEO (Alon Kaufman), funding history, target industries (financial services, healthcare, insurance, telecom)